├── README.md
└── markdown2html_py
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    ├── fontawesome-animations.txt
    ├── html_foot.txt
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    ├── index.html
    └── markdown2html.py


/README.md:
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  1 | # :balloon: :tada: Deep Learning Drizzle :confetti_ball: :balloon:
  2 | 
  3 | :books: [**"Read enough so you start developing intuitions and then trust your intuitions and go for it!"** ](https://www.deeplearning.ai/hodl-geoffrey-hinton/) :books:  ​<br/>  Prof. Geoffrey Hinton, University of Toronto
  4 | 
  5 | :heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign:
  6 | 
  7 | ### Contents
  8 | :heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign:
  9 | 
 10 | |                                                              |                                                              |
 11 | | ------------------------------------------------------------ | ------------------------------------------------------------ |
 12 | | **Deep Learning (Deep Neural Networks)**  [:arrow_heading_down:](https://github.com/kmario23/deep-learning-drizzle#tada-deep-learning-deep-neural-networks-confetti_ball-balloon) | **Probabilistic Graphical Models**  [:arrow_heading_down:](https://github.com/kmario23/deep-learning-drizzle#loudspeaker-probabilistic-graphical-models-sparkles) |
 13 | |                                                              |                                                              |
 14 | | **Machine Learning Fundamentals**  [:arrow_heading_down:](https://github.com/kmario23/deep-learning-drizzle#cupid-machine-learning-fundamentals-cyclone-boom) | **Natural Language Processing**  [:arrow_heading_down:](https://github.com/kmario23/deep-learning-drizzle#hibiscus-natural-language-processing-cherry_blossom-sparkling_heart) |
 15 | |                                                              |                                                              |
 16 | | **Optimization for Machine Learning**  [:arrow_heading_down:](https://github.com/kmario23/deep-learning-drizzle#cupid-optimization-for-machine-learning-cyclone-boom) | **Automatic Speech Recognition** [:arrow_heading_down:](https://github.com/kmario23/deep-learning-drizzle#speaking_head-automatic-speech-recognition-speech_balloon-thought_balloon) |
 17 | |                                                              |                                                              |
 18 | | **General Machine Learning**  [:arrow_heading_down:](https://github.com/kmario23/deep-learning-drizzle#cupid-general-machine-learning-cyclone-boom) | **Modern Computer Vision** [:arrow_heading_down:](https://github.com/kmario23/deep-learning-drizzle#fire-modern-computer-vision-camera_flash-movie_camera) |
 19 | |                                                              |                                                              |
 20 | | **Reinforcement Learning**  [:arrow_heading_down:](https://github.com/kmario23/deep-learning-drizzle#balloon-reinforcement-learning-hotsprings-video_game) | **Boot Camps or Summer Schools** [:arrow_heading_down:](https://github.com/kmario23/deep-learning-drizzle#star2-boot-camps-or-summer-schools-maple_leaf) |
 21 | |                                                              |                                                              |
 22 | | **Bayesian Deep Learning** [:arrow_heading_down:](https://github.com/kmario23/deep-learning-drizzle#game_die-bayesian-deep-learning-spades-gem) | **Medical Imaging** [:arrow_heading_down:](https://github.com/kmario23/deep-learning-drizzle#movie_camera-medical-imaging-camera-video_camera) |
 23 | |                                                              |                                                              |
 24 | | **Graph Neural Networks** [:arrow_heading_down: ](https://github.com/kmario23/deep-learning-drizzle#tada-graph-neural-networks-geometric-dl-confetti_ball-balloon) | **Bird's-eye view of Artificial Intelligence** [:arrow_heading_down:](https://github.com/kmario23/deep-learning-drizzle#bird-birds-eye-view-of-agi-eagle) |
 25 | |                                                              |                                                              |
 26 | 
 27 | :heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign:
 28 | ## :tada: Deep Learning (Deep Neural Networks) :confetti_ball: :balloon: 
 29 | :heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign:
 30 | 
 31 | | S.No | Course Name                                           | University/Instructor(s)                       | Course WebPage                                               | Lecture Videos                                               | Year            |
 32 | | ---- | ----------------------------------------------------- | ---------------------------------------------- | ------------------------------------------------------------ | ------------------------------------------------------------ | --------------- |
 33 | | 1.   | **Neural Networks for Machine Learning**              | Geoffrey Hinton, University of Toronto         | [Lecture-Slides](http://www.cs.toronto.edu/~hinton/coursera_slides.html) <br/> [CSC321-tijmen](https://www.cs.toronto.edu/~tijmen/csc321/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLoRl3Ht4JOcdU872GhiYWf6jwrk_SNhz9) <br/> [UofT-mirror](https://www.cs.toronto.edu/~hinton/coursera_lectures.html) | 2012 <br/> 2014 |
 34 | | 2.   | **Neural Networks Demystified**                       | Stephen Welch, Welch Labs                      | [Suppl. Code](https://github.com/stephencwelch/Neural-Networks-Demystified) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLiaHhY2iBX9hdHaRr6b7XevZtgZRa1PoU) | 2014            |
 35 | | 3.   | **Deep Learning at Oxford**                           | Nando de Freitas, Oxford University            | [Oxford-ML](http://www.cs.ox.ac.uk/teaching/courses/2014-2015/ml/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLE6Wd9FR--EfW8dtjAuPoTuPcqmOV53Fu) | 2015            |
 36 | | 4.   | **Deep Learning for Perception**                      | Dhruv Batra, Virginia Tech                     | [ECE-6504](https://computing.ece.vt.edu/~f15ece6504/)        | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL-fZD610i7yAsfH2eLBiRDa90kL2ML0f7) | 2015            |
 37 | | 5.   | **Deep Learning**                                     | Ali Ghodsi, University of Waterloo             | [STAT-946](https://uwaterloo.ca/data-analytics/deep-learning) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLehuLRPyt1Hyi78UOkMPWCGRxGcA9NVOE) | F2015           |
 38 | | 6.   | **CS231n: CNNs for Visual Recognition**               | Andrej Karpathy, Stanford University           | [CS231n](http://cs231n.stanford.edu/2015/)                   | `None`                                                       | 2015            |
 39 | | 7.   | **CS224d: Deep Learning for NLP**                     | Richard Socher, Stanford University            | [CS224d](http://cs224d.stanford.edu)                         | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLmImxx8Char8dxWB9LRqdpCTmewaml96q) | 2015            |
 40 | | 8.   | **Bay Area Deep Learning**                            | Many legends, Stanford                         | `None`                                                       | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLrAXtmErZgOfMuxkACrYnD2fTgbzk2THW) | 2016            |
 41 | | 9.   | **CS231n: CNNs for Visual Recognition**               | Andrej Karpathy, Stanford University           | [CS231n](http://cs231n.stanford.edu/2016/)                   | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLkt2uSq6rBVctENoVBg1TpCC7OQi31AlC) <br/>[(Academic Torrent)](https://academictorrents.com/details/46c5af9e2075d9af06f280b55b65cf9b44eb9fe7) | 2016            |
 42 | | 10.  | **Neural Networks**                                   | Hugo Larochelle, Université de Sherbrooke      | [Neural-Networks](http://info.usherbrooke.ca/hlarochelle/neural_networks/content.html) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL6Xpj9I5qXYEcOhn7TqghAJ6NAPrNmUBH) <br/> [(Academic Torrent)](https://academictorrents.com/details/e046bca3bc837053d1609ef33d623ee5c5af7300) | 2016            |
 43 | |      |                                                       |                                                |                                                              |                                                              |                 |
 44 | | 11.  | **CS224d: Deep Learning for NLP**                     | Richard Socher, Stanford University            | [CS224d](http://cs224d.stanford.edu)                         | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLlJy-eBtNFt4CSVWYqscHDdP58M3zFHIG) <br/>[(Academic Torrent)](https://academictorrents.com/details/dd9b74b50a1292b4b154094b7338ec1d66e8894d) | 2016            |
 45 | | 12.  | **CS224n: NLP with Deep Learning**                    | Richard Socher, Stanford University            | [CS224n](http://web.stanford.edu/class/cs224n/)              | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL3FW7Lu3i5Jsnh1rnUwq_TcylNr7EkRe6) | 2017            |
 46 | | 13.  | **CS231n: CNNs for Visual Recognition**               | Justin Johnson, Stanford University            | [CS231n](http://cs231n.stanford.edu/2017/)                   | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL3FW7Lu3i5JvHM8ljYj-zLfQRF3EO8sYv) <br/> [(Academic Torrent)](https://academictorrents.com/details/ed8a16ebb346e14119a03371665306609e485f13) | 2017            |
 47 | | 14.  | **Topics in Deep Learning**                           | Ruslan Salakhutdinov, CMU                      | [10707](https://deeplearning-cmu-10707.github.io/)           | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLpIxOj-HnDsOSL__Buy7_UEVQkyfhHapa) | F2017           |
 48 | | 15.  | **Deep Learning Crash Course**                        | Leo Isikdogan, UT Austin                       | `None`                                                       | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLWKotBjTDoLj3rXBL-nEIPRN9V3a9Cx07) | 2017            |
 49 | | 16.  | **Deep Learning and its Applications**                | François Pitié, Trinity College Dublin         | [EE4C16](https://github.com/frcs/4C16-2017)                  | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLIo1iEzl5iB9NkulNR0X5vXN8AaEKglWT) | 2017            |
 50 | | 17.  | **Deep Learning**                                     | Andrew Ng, Stanford University                 | [CS230](http://cs230.stanford.edu/)                          | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLoROMvodv4rOABXSygHTsbvUz4G_YQhOb) | 2018            |
 51 | | 18.  | **UvA Deep Learning**                                 | Efstratios Gavves, University of Amsterdam     | [UvA-DLC](https://uvadlc.github.io/)                         | [Lecture-Videos](https://uvadlc.github.io/lectures-sep2018.html) | 2018            |
 52 | | 19.  | **Advanced Deep Learning and Reinforcement Learning** | Many legends, DeepMind                         | `None`                                                       | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLqYmG7hTraZDNJre23vqCGIVpfZ_K2RZs) | 2018            |
 53 | | 20.  | **Machine Learning**                                  | Peter Bloem, Vrije Universiteit Amsterdam      | [MLVU](https://mlvu.github.io/)                              | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLCof9EqayQgsORO3pFzeYZFz6cszYO0VJ) | 2018            |
 54 | |      |                                                       |                                                |                                                              |                                                              |                 |
 55 | | 21.  | **Deep Learning**                                     | Francois Fleuret, EPFL                         | [EE-59](https://fleuret.org/ee559-2018/dlc)                  | [Video-Lectures](https://fleuret.org/ee559-2018/dlc/#materials) | 2018            |
 56 | | 22.  | **Introduction to Deep Learning**                     | Alexander Amini, Harini Suresh and others, MIT | [6.S191](http://introtodeeplearning.com/)                    | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLtBw6njQRU-rwp5__7C0oIVt26ZgjG9NI) <br/> [2017-version](https://www.youtube.com/playlist?list=PLkkuNyzb8LmxFutYuPA7B4oiMn6cjD6Rs) | 2017- 2021     |
 57 | | 23.  | **Deep Learning for Self-Driving Cars**               | Lex Fridman, MIT                               | [6.S094](https://selfdrivingcars.mit.edu/)                   | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLrAXtmErZgOeiKm4sgNOknGvNjby9efdf) | 2017-2018       |
 58 | | 24.  | **Introduction to Deep Learning**                     | Bhiksha Raj and many others, CMU               | [11-485/785](http://deeplearning.cs.cmu.edu/)                | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLp-0K3kfddPwJBJ4Q8We-0yNQEG0fZrSa) | S2018           |
 59 | | 25.  | **Introduction to Deep Learning**                     | Bhiksha Raj and many others, CMU               | [11-485/785](http://deeplearning.cs.cmu.edu/)                | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLp-0K3kfddPyH44FP0dl0CbYprvTcfgOI)   [Recitation-Inclusive](https://www.youtube.com/playlist?list=PLLR0_ZOlbfD6KDBq93G8-guHI-J1ICeFm) | F2018           |
 60 | | 26.  | **Deep Learning Specialization**                      | Andrew Ng, Stanford                            | [DL.AI](https://www.deeplearning.ai/deep-learning-specialization/) | [YouTube-Lectures](https://www.youtube.com/channel/UCcIXc5mJsHVYTZR1maL5l9w/playlists) | 2017-2018       |
 61 | | 27.  | **Deep Learning**                                     | Ali Ghodsi, University of Waterloo             | [STAT-946](https://uwaterloo.ca/data-analytics/teaching/deep-learning-2017) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLehuLRPyt1HxTolYUWeyyIoxDabDmaOSB) | F2017           |
 62 | | 28.  | **Deep Learning**                                     | Mitesh Khapra, IIT-Madras                      | [CS7015](https://www.cse.iitm.ac.in/~miteshk/CS7015.html)    | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLyqSpQzTE6M9gCgajvQbc68Hk_JKGBAYT) | 2018            |
 63 | | 29.  | **Deep Learning for AI**                              | UPC Barcelona                                  | [DLAI-2017](https://telecombcn-dl.github.io/2017-dlai/) <br/> [DLAI-2018](https://telecombcn-dl.github.io/2018-dlai/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL-5eMc3HQTBagIUjKefjcTbnXC0wXC_vd) | 2017-2018       |
 64 | | 30.  | **Deep Learning**                                     | Alex Bronstein and Avi Mendelson, Technion     | [CS236605](https://vistalab-technion.github.io/cs236605/info/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLM0a6Z788YAZuqg2Ip-_dPLzEd33lZvP2) | 2018            |
 65 | |      |                                                       |                                                |                                                              |                                                              |                 |
 66 | | 31.  | **MIT Deep Learning**                                 | Many Researchers,  Lex Fridman, MIT            | [6.S094, 6.S091, 6.S093](https://deeplearning.mit.edu/)      | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLrAXtmErZgOeiKm4sgNOknGvNjby9efdf) | 2019            |
 67 | | 32.  | **Deep Learning Book** companion videos               | Ian Goodfellow and others                      | [DL-book slides](https://www.deeplearningbook.org/lecture_slides.html) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLsXu9MHQGs8df5A4PzQGw-kfviylC-R9b) | 2017            |
 68 | | 33.  | **Theories of Deep Learning**                         | Many Legends, Stanford                         | [Stats-385](https://stats385.github.io/)                     | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLwUqqMt5en7fFLwSDa9V3JIkDam-WWgqy) <br/> (first 10 lectures) | F2017           |
 69 | | 34.  | **Neural Networks**                                   | Grant Sanderson                                | `None`                                                       | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLZHQObOWTQDNU6R1_67000Dx_ZCJB-3pi) | 2017-2018       |
 70 | | 35.  | **CS230: Deep Learning**                              | Andrew Ng, Kian Katanforoosh, Stanford         | [CS230](http://cs230.stanford.edu/)                          | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLoROMvodv4rOABXSygHTsbvUz4G_YQhOb) | A2018           |
 71 | | 36.  | **Theory of Deep Learning**                           | Lots of Legends, Canary Islands                | [DALI'18](http://dalimeeting.org/dali2018/workshopTheoryDL.html) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLeCNfJWZKqxtWBnV8gefGqmmPgz9YF4LR) | 2018            |
 72 | | 37.  | **Introduction to Deep Learning**                     | Alex Smola, UC Berkeley                        | [Stat-157](http://courses.d2l.ai/berkeley-stat-157/index.html) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLZSO_6-bSqHQHBCoGaObUljoXAyyqhpFW) | S2019           |
 73 | | 38.  | **Deep Unsupervised Learning**                        | Pieter Abbeel, UC Berkeley                     | [CS294-158](https://sites.google.com/view/berkeley-cs294-158-sp19/home) | [YouTube-Lectures](https://www.youtube.com/channel/UCf4SX8kAZM_oGcZjMREsU9w/videos) | S2019           |
 74 | | 39.  | **Machine Learning**                                  | Peter Bloem, Vrije Universiteit Amsterdam      | [MLVU](https://mlvu.github.io/)                              | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLCof9EqayQgupldnTvqNy_BThTcME5r93) | 2019            |
 75 | | 40.  | **Deep Learning on Computational Accelerators**       | Alex Bronstein and Avi Mendelson, Technion     | [CS236605](https://vistalab-technion.github.io/cs236605/lectures/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLM0a6Z788YAa_WCy_V-q9NrGm5qQegZR5) | S2019           |
 76 | |      |                                                       |                                                |                                                              |                                                              |                 |
 77 | | 41.  | **Introduction to Deep Learning**                     | Bhiksha Raj and many others, CMU               | [11-785](http://www.cs.cmu.edu/~bhiksha/courses/deeplearning/Spring.2019/www) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLp-0K3kfddPzNdZPX4p0lVi6AcDXBofuf) | S2019           |
 78 | | 42.  | **Introduction to Deep Learning**                     | Bhiksha Raj and many others, CMU               | [11-785](https://www.cs.cmu.edu/~bhiksha/courses/deeplearning/Fall.2019/www) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLp-0K3kfddPwz13VqV1PaMXF6V6dYdEsj) <br> [Recitations](https://www.youtube.com/playlist?list=PLp-0K3kfddPxf4T59JEQKv5UanLPVsxzz) | F2019           |
 79 | | 43.  | **UvA Deep Learning**                                 | Efstratios Gavves, University of Amsterdam     | [UvA-DLC](https://uvadlc.github.io/)                         | [Lecture-Videos](https://uvadlc.github.io/lectures-apr2019.html) | S2019           |
 80 | | 44. | **Deep Learning** | Prabir Kumar Biswas, IIT Kgp | `None` | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLbRMhDVUMngc7NM-gDwcBzIYZNFSK2N1a) | 2019 |
 81 | | 45. | **Deep Learning and its Applications** | Aditya Nigam, IIT Mandi | [CS-671](http://faculty.iitmandi.ac.in/~aditya/cs671/index.html) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLKvX2d3IUq586Ic9gIhZj6ubpWV-OJfl4) | 2019 |
 82 | | 46. | **Neural Networks**                                   | Neil Rhodes, Harvey Mudd College               | [CS-152](https://www.cs.hmc.edu/~rhodes/cs152/schedule.html) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLgEuVSRbAI9UIQSHGy4l01laA_12YOqEj) | F2019           |
 83 | | 47. | **Deep Learning**                                     | Thomas Hofmann, ETH Zürich                     | [DAL-DL](http://www.da.inf.ethz.ch/teaching/2019/DeepLearning) | [Lecture-Videos](https://video.ethz.ch/lectures/d-infk/2019/autumn/263-3210-00L.html) | F2019           |
 84 | | 48. | **Deep Learning**                                     | Milan Straka, Charles University               | [NPFL114](https://ufal.mff.cuni.cz/courses/npfl114) | [Lecture-Videos](https://ufal.mff.cuni.cz/courses/npfl114/1718-summer) | S2019 |
 85 | | 49. | **UvA Deep Learning** | Efstratios Gavves, University of Amsterdam | [UvA-DLC-19](https://uvadlc.github.io/#lectures) | [Lecture-Videos](https://uvadlc.github.io/#lectures) | F2019 |
 86 | | 50. | **Artificial Intelligence: Principles and Techniques** | Percy Liang and Dorsa Sadigh, Stanford University | [CS221](https://stanford-cs221.github.io/autumn2019/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLoROMvodv4rO1NB9TD4iUZ3qghGEGtqNX) | F2019 |
 87 | |  |  |  |  |  |  |
 88 | | 51. | **Analyses of Deep Learning** | Lots of Legends, Stanford University | [STATS-385](https://stats385.github.io/) | [YouTube-Lectures](https://stats385.github.io/lecture_videos) | 2017-2019 |
 89 | | 52. | **Deep Learning Foundations and Applications** | Debdoot Sheet and Sudeshna Sarkar, IIT-Kgp | [AI61002](http://www.facweb.iitkgp.ac.in/~debdoot/courses/AI61002/Spr2020) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL_AdDfjIMo6pZfwjZ0rJlkE_MIsmRW7Mh) | S2020 |
 90 | | 53. | **Designing, Visualizing, and Understanding Deep Neural Networks** | John Canny, UC Berkeley | [CS 182/282A](https://bcourses.berkeley.edu/courses/1487769/pages/cs-l-w-182-slash-282a-designing-visualizing-and-understanding-deep-neural-networks-spring-2020) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLkFD6_40KJIwaO6Eca8kzsEFBob0nFvwm) | S2020 |
 91 | | 54. | **Deep Learning** | Yann LeCun and Alfredo Canziani, NYU | [DS-GA 1008](https://atcold.github.io/pytorch-Deep-Learning/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLLHTzKZzVU9eaEyErdV26ikyolxOsz6mq) | S2020 |
 92 | | 55. | **Introduction to Deep Learning** | Bhiksha Raj, CMU | [11-785](https://deeplearning.cs.cmu.edu/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLp-0K3kfddPzCnS4CqKphh-zT3aDwybDe) | S2020 |
 93 | | 56. | **Deep Unsupervised Learning** | Pieter Abbeel, UC Berkeley | [CS294-158](https://sites.google.com/view/berkeley-cs294-158-sp20) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLwRJQ4m4UJjPiJP3691u-qWwPGVKzSlNP) | S2020 |
 94 | | 57. | **Machine Learning** | Peter Bloem, Vrije Universiteit Amsterdam | [VUML](https://mlvu.github.io/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLCof9EqayQgthR7IViXkAkUwel_rhxGYM) | S2020 |
 95 | | 58. | **Deep Learning (with PyTorch)** | Alfredo Canziani and Yann LeCun, NYU | [DS-GA 1008](https://atcold.github.io/pytorch-Deep-Learning/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLLHTzKZzVU9eaEyErdV26ikyolxOsz6mq) | S2020 |
 96 | | 59. | **Introduction to Deep Learning and Generative Models** | Sebastian Raschka, UW-Madison | [Stat453](http://pages.stat.wisc.edu/~sraschka/teaching/stat453-ss2020/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLTKMiZHVd_2JkR6QtQEnml7swCnFBtq4P) | S2020 |
 97 | | 60. | **Deep Learning** | Andreas Maier, FAU Erlangen-Nürnberg | [DL-2020](https://www.video.uni-erlangen.de/course/id/925) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLpOGQvPCDQzvgpD3S0vTy7bJe2pf_yJFj) <br/>[Lecture-Videos](https://www.video.uni-erlangen.de/course/id/925) | SS2020 |
 98 | |  |  |  |  |  |  |
 99 | | 61. | **Introduction to Deep Learning** | Laura Leal-Taixé and Matthias Niessner, TU-München | [I2DL-IN2346](https://dvl.in.tum.de/teaching/i2dl-ss20/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLQ8Y4kIIbzy_OaXv86lfbQwPHSomk2o2e) | SS2020 |
100 | | 62. | **Deep Learning** | Sargur Srihari, SUNY-Buffalo | [CSE676](https://cedar.buffalo.edu/~srihari/CSE676/) | [YouTube-Lectures-P1](https://www.youtube.com/playlist?list=PLmx4utxjUQD70k_NzeiSIXf30m54T_e1h) <br/>[YouTube-Lectures-P2](https://www.youtube.com/channel/UCUm7yUmVJyAbYh_0ppJ4H-g/videos) | 2020 |
101 | | 63. | **Deep Learning Lecture Series** | Lots of Legends, DeepMind x UCL, London | [DLLS-20](https://deepmind.com/learning-resources/deep-learning-lecture-series-2020) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLqYmG7hTraZCDxZ44o4p3N5Anz3lLRVZF) | 2020 |
102 | | 64. | **MultiModal Machine Learning** | Louis-Philippe Morency & others, Carnegie Mellon University | [11-777 MMML-20](https://cmu-multicomp-lab.github.io/mmml-course/fall2020) | [YouTube-Lectures](https://www.youtube.com/channel/UCqlHIJTGYhiwQpNuPU5e2gg/videos) | F2020 |
103 | | 65. | **Reliable and Interpretable Artificial Intelligence** | Martin Vechev, ETH Zürich | [RIAI-20](https://www.sri.inf.ethz.ch/teaching/riai2020) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLWjm4hHpaNg6c-W7JjNYDEC_kJK9oSp0Y) | F2020 |
104 | | 66. | **Fundamentals of Deep Learning** | David McAllester, Toyota Technological Institute, Chicago | [TTIC-31230](https://mcallester.github.io/ttic-31230/Fall2020) | [YouTube-Lectures](https://www.youtube.com/channel/UCciVrtrRR3bQdaGbti9-hVQ/videos) | F2020 |
105 | | 67. | **Foundations of Deep Learning** | Soheil Feize, University of Maryland, College Park | [CMSC 828W](http://www.cs.umd.edu/class/fall2020/cmsc828W) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLHgjs9ncvHi80UCSlSvQe-TK_uOyDv_Jf) | F2020 |
106 | | 68. | **Deep Learning** | Andreas Geiger, Universität Tübingen | [DL-UT](https://uni-tuebingen.de/fakultaeten/mathematisch-naturwissenschaftliche-fakultaet/fachbereiche/informatik/lehrstuehle/autonomous-vision/teaching/lecture-deep-learning/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL05umP7R6ij3NTWIdtMbfvX7Z-4WEXRqD) | W20/21 |
107 | | 69. | **Deep Learning** | Andreas Maier, FAU Erlangen-Nürnberg | [DL-FAU](https://www.fau.tv/course/id/1599) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLpOGQvPCDQzvJEPFUQ3mJz72GJ95jyZTh) | W20/21 |
108 | | 70. | **Fundamentals of Deep Learning** | Terence Parr and Yannet Interian, University of San Francisco | [DL-Fundamentals](https://github.com/parrt/fundamentals-of-deep-learning) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLFCc_Fc116ikeol9CZcWWKqmrJljxhE4N) | S2021 |
109 | |  |  |  |  |  |  |
110 | | 71. | **Full Stack Deep Learning** | Pieter Abbeel, Sergey Karayev, UC Berkeley | [FS-DL](https://fullstackdeeplearning.com/spring2021) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL1T8fO7ArWlcWg04OgNiJy91PywMKT2lv) | S2021 |
111 | | 72. | **Deep Learning: Designing, Visualizing, and Understanding DNNs** | Sergey Levine, UC Berkeley | [CS 182](https://cs182sp21.github.io) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL_iWQOsE6TfVmKkQHucjPAoRtIJYt8a5A) | S2021 |
112 | | 73. | **Deep Learning in the Life Sciences** | Manolis Kellis, MIT | [6.874](https://mit6874.github.io) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLypiXJdtIca5sxV7aE3-PS9fYX3vUdIOX) | S2021 |
113 | | 74. | **Introduction to Deep Learning and Generative Models** | Sebastian Raschka, University of Wisconsin-Madison | [Stat 453](http://pages.stat.wisc.edu/~sraschka/teaching/stat453-ss2021) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLTKMiZHVd_2KJtIXOW0zFhFfBaJJilH51) | S2021 |
114 | | 75. | **Deep Learning** | Alfredo Canziani and Yann LeCun, NYU | [NYU-DLSP21](https://atcold.github.io/NYU-DLSP21) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLLHTzKZzVU9e6xUfG10TkTWApKSZCzuBI) | S2021 |
115 | | 76. | **Applied Deep Learning** | Alexander Pacha, TU Wien | `None` | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLNsFwZQ_pkE8xNYTEyorbaWPN7nvbWyk1) | 2020-2021 |
116 | | 77. | **Machine Learning** | Hung-yi Lee, National Taiwan University | [ML'21](https://speech.ee.ntu.edu.tw/~hylee/ml/2021-spring.php) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLJV_el3uVTsNxV_IGauQZBHjBKZ26JHjd) | S2021 |
117 | | 78. | **Mathematics of Deep Learning** | Lots of legends, FAU | [MoDL](https://www.fau.tv/course/id/878) | [Lecture-Videos](https://www.fau.tv/course/id/878) | 2019-21 |
118 | | 79. | **Deep Learning** | Peter Bloem, Michael Cochez, and Jakub Tomczak, VU-Amsterdam | [DL](https://dlvu.github.io/) | [YouTube-Lectures](https://www.youtube.com/channel/UCYh1zKnwzrSjrO2Ae-akfTg/playlists) | 2020-21 |
119 | | 80. | **Applied Deep Learning** | Maziar Raissi, UC Boulder | [ADL'21](https://github.com/maziarraissi/Applied-Deep-Learning) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLoEMreTa9CNmuxQeIKWaz7AVFd_ZeAcy4) | 2021 |
120 | | | | | | | |
121 | | 81. | **An Introduction to Group Equivariant Deep Learning** | Erik J. Bekkers, Universiteit van Amsterdam | [UvAGEDL](https://uvagedl.github.io) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL8FnQMH2k7jzPrxqdYufoiYVHim8PyZWd) | 2022 |
122 | | | | | | | |
123 | 
124 | [Go to Contents :arrow_heading_up:](https://github.com/kmario23/deep-learning-drizzle#contents) 
125 | 
126 | :heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign:
127 | ### :cupid: Machine Learning Fundamentals :cyclone: :boom: 
128 | :heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign:
129 | 
130 | | S.No | Course Name                                                  | University/Instructor(s)                                | Course Webpage                                               | Video Lectures                                               | Year       |
131 | | ---- | ------------------------------------------------------------ | ------------------------------------------------------- | ------------------------------------------------------------ | ------------------------------------------------------------ | ---------- |
132 | | 1.   | **Linear Algebra**                                           | Gilbert Strang, MIT                                     | [18.06 SC](http://ocw.mit.edu/18-06SCF11)                    | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL221E2BBF13BECF6C) | 2011       |
133 | | 2.   | **Probability Primer**                                       | Jeffrey Miller, Brown University                        | `mathematical monk`                                          | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL17567A1A3F5DB5E4) | 2011       |
134 | | 3.   | **Information Theory, Pattern Recognition, and Neural Networks** | David Mackay, University of Cambridge                   | [ITPRNN](http://www.inference.org.uk/mackay/itprnn)          | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLruBu5BI5n4aFpG32iMbdWoRVAA-Vcso6) | 2012       |
135 | | 4.   | **Linear Algebra Review**                                    | Zico Kolter, CMU                                        | [LinAlg](http://www.cs.cmu.edu/~zkolter/course/linalg/index.html) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLM4Pv4KYYzGzL5ay6dmpyzRnbzQ__8v_t) | 2013       |
136 | | 5.   | **Probability and Statistics**                               | Michel van Biezen                                       | `None`                                                       | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLX2gX-ftPVXUWwTzAkOhBdhplvz0fByqV) | 2015       |
137 | | 6.   | **Linear Algebra: An in-depth Introduction**                 | Pavel Grinfeld                                          | `None`                                                       | [Part-1](https://www.youtube.com/playlist?list=PLlXfTHzgMRUKXD88IdzS14F4NxAZudSmv) <br/> [Part-2](https://www.youtube.com/playlist?list=PLlXfTHzgMRULWJYthculb2QWEiZOkwTSU)  <br/> [Part-3](https://www.youtube.com/playlist?list=PLlXfTHzgMRUIqYrutsFXCOmiqKUgOgGJ5) <br/> [Part-4](https://www.youtube.com/playlist?list=PLlXfTHzgMRULZfrNCrrJ7xDcTjGr633mm) | 2015- 2017 |
138 | | 7.   | **Multivariable Calculus**                                   | Grant Sanderson, Khan Academy                           | `None`                                                       | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLSQl0a2vh4HC5feHa6Rc5c0wbRTx56nF7) | 2016       |
139 | | 8.   | **Essence of Linear Algebra**                                | Grant Sanderson                                         | `None`                                                       | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLZHQObOWTQDPD3MizzM2xVFitgF8hE_ab) | 2016       |
140 | | 9.   | **Essence of Calculus**                                      | Grant Sanderson                                         | `None`                                                       | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLZHQObOWTQDMsr9K-rj53DwVRMYO3t5Yr) | 2017-2018  |
141 | | 10.  | **Math Background for Machine Learning**                     | Geoff Gordon, CMU                                       | [10-606](https://canvas.cmu.edu/courses/603/assignments/syllabus), [10-607](https://piazza.com/cmu/fall2017/1060610607/home) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL7y-1rk2cCsAqRtWoZ95z-GMcecVG5mzA) | F2017      |
142 | |      |                                                              |                                                         |                                                              |                                                              |            |
143 | | 11.  | **Mathematics for Machine Learning** (Linear Algebra, Calculus) | David Dye, Samuel Cooper, and Freddie Page, IC-London   | [MML](https://www.coursera.org/learn/linear-algebra-machine-learning) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLmAuaUS7wSOP-iTNDivR0ANKuTUhEzMe4) | 2018       |
144 | | 12.  | **Multivariable Calculus**                                   | S.K. Gupta and Sanjeev Kumar, IIT-Roorkee               | [MVC](https://nptel.ac.in/syllabus/111107108/)               | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLq-Gm0yRYwTiQtK374NzhFOcQkWmJ71vx) | 2018       |
145 | | 13.  | **Engineering Probability**                                  | Rich Radke, Rensselaer Polytechnic Institute            | `None`                                                       | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLuh62Q4Sv7BU1dN2G6ncyiMbML7OXh_Jx) | 2018       |
146 | | 14.  | **Matrix Methods in Data Analysis, Signal Processing, and Machine Learning** | Gilbert Strang, MIT                                     | [18.065](https://ocw.mit.edu/courses/mathematics/18-065-matrix-methods-in-data-analysis-signal-processing-and-machine-learning-spring-2018) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLUl4u3cNGP63oMNUHXqIUcrkS2PivhN3k) | S2018      |
147 | | 15.  | **Information Theory**                                       | Himanshu Tyagi, IISC, Bengaluru                         | [E2 201](https://ece.iisc.ac.in/~htyagi/course-E2201-2020.html) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLgMDNELGJ1CYS-8dlMGPIaowVfeda4nUj) | 2018-20    |
148 | | 16.  | **Math Camp**                                                | Mark Walker, University of Arizona                      | [UAMathCamp / Econ-519](http://www.u.arizona.edu/~mwalker/MathCamp2019.htm) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLcjqUUQt__ZGLhwUacPm7_RKs2eJNFwco) | 2019       |
149 | | 17.  | **A 2020 Vision of Linear Algebra**                          | Gilbert Strang, MIT                                     | [VoLA](https://ocw.mit.edu/resources/res-18-010-a-2020-vision-of-linear-algebra-spring-2020/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLUl4u3cNGP61iQEFiWLE21EJCxwmWvvek) | S2020      |
150 | | 18.  | **Mathematics for Numerical Computing and Machine Learning** | Szymon Rusinkiewicz, Princeton University               | [COS-302](https://www.cs.princeton.edu/courses/archive/fall20/cos302/outline.html) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL88aSuXxl_dSjC5pIG8bGkC5wsUPyW_Hh) | F2020      |
151 | | 19.  | **Essential Statistics for Neuroscientists**                 | Philipp Berens, Universität Klinikum Tübingen           | `None`                                                       | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL05umP7R6ij0Gw5SLIrOA1dMYScCx4oXT) | 2020       |
152 | | 20.  | **Mathematics for Machine Learning**                         | Ulrike von Luxburg, Eberhard Karls Universität Tübingen | [Math4ML](https://www.tml.cs.uni-tuebingen.de/teaching/2020_maths_for_ml) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL05umP7R6ij1a6KdEy8PVE9zoCv6SlHRS) | W2020      |
153 | | 21.  | **Introduction to Causal Inference**                         | Brady Neal, Mila, Montréal                              | [CausalInf](https://www.bradyneal.com/causal-inference-course) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLoazKTcS0Rzb6bb9L508cyJ1z-U9iWkA0) | F2020      |
154 | | 22.  | **Applied Linear Algebra**                                   | Andrew Thangaraj, IIT Madras                            | [EE5120](http://www.ee.iitm.ac.in/~andrew/EE5120)            | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLyqSpQzTE6M-CHZU5RGfamcXOnuFyTOpm) | 2021       |
155 | | 23.  | **Mathematical Tools for Data Science**                      | Carlos Fernandez-Granda, New York University            | [DS-GA 1013/Math-GA 2824](https://cds.nyu.edu/math-tools)    | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLBEf5mJtE6KtU6YlXFZD6lyYcHhW5pIlc) | 2021       |
156 | | 24.  | **Mathematics for Numerical Computing and Machine Learning** | Ryan Adams, Princeton University                        | [COS 302 / SML 305](https://www.cs.princeton.edu/courses/archive/spring21/cos302) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLCO4cUaBLHFEHo42HVIVWaSOvbAiH30uc) | 2021       |
157 | |      |                                                              |                                                         |                                                              |                                                              |            |
158 | |      |                                                              |                                                         |                                                              |                                                              |            |
159 | 
160 | [Go to Contents :arrow_heading_up:](https://github.com/kmario23/deep-learning-drizzle#contents) 
161 | 
162 | :heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign:
163 | ### :cupid: Optimization for Machine Learning :cyclone: :boom: 
164 | :heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign:
165 | 
166 | | S.No | Course Name                                                  | University/Instructor(s)                                     | Course Webpage                                               | Video Lectures                                               | Year       |
167 | | ---- | ------------------------------------------------------------ | ------------------------------------------------------------ | ------------------------------------------------------------ | ------------------------------------------------------------ | ---------- |
168 | | 1.   | **Convex Optimization**                                      | Stephen Boyd, Stanford University                            | [ee364a](http://web.stanford.edu/class/ee364a/lectures.html) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL3940DD956CDF0622) | 2008       |
169 | | 2.   | **Introduction to Optimization**                             | Michael Zibulevsky, Technion                                 | [CS-236330](https://sites.google.com/site/michaelzibulevsky/optimization-course) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLDFB2EEF4DDAFE30B) | 2009       |
170 | | 3.   | **Optimization for Machine Learning**                        | S V N Vishwanathan, Purdue University                        | `None`                                                       | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL09B0E8AFC69BE108) | 2011       |
171 | | 4.   | **Optimization**                                             | Geoff Gordon & Ryan Tibshirani, CMU                          | [10-725](https://www.cs.cmu.edu/~ggordon/10725-F12/)         | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL7y-1rk2cCsDOv91McLOnV4kExFfTB7dU) | 2012       |
172 | | 5.   | **Convex Optimization**                                      | Joydeep Dutta, IIT-Kanpur                                    | [cvx-nptel](https://nptel.ac.in/courses/111/104/111104068)   | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLbMVogVj5nJQHFqfiSdgaLCCWvDcm1W4l) | 2013       |
173 | | 6.   | **Foundations of Optimization**                              | Joydeep Dutta, IIT-Kanpur                                    | [fop-nptel](https://nptel.ac.in/courses/111/104/111104071)   | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLbMVogVj5nJRRbofh3Qm3P6_NVyevDGD_) | 2014       |
174 | | 7.   | **Algorithmic Aspects of Machine Learning**                  | Ankur Moitra, MIT                                            | [18.409-AAML](http://people.csail.mit.edu/moitra/409.html)   | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLB3sDpSRdrOvI1hYXNsa6Lety7K8FhPpx) | S2015      |
175 | | 8.   | **Numerical Optimization**                                   | Shirish K. Shevade, IISC                                     | `None`                                                       | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL6EA0722B99332589) | 2015       |
176 | | 9.   | **Convex Optimization**                                      | Ryan Tibshirani, CMU                                         | [10-725](https://www.stat.cmu.edu/~ryantibs/convexopt-S15/)  | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLjbUi5mgii6BZBhJ9nW7eydgycyCOYeZ6) | S2015      |
177 | | 10.  | **Convex Optimization**                                      | Ryan Tibshirani, CMU                                         | [10-725](http://stat.cmu.edu/~ryantibs/convexopt-F15/)       | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLjbUi5mgii6AGJW3La3BpEXe27n8v3biT) | F2015      |
178 | | 11.  | **Advanced Algorithms**                                      | Ankur Moitra, MIT                                            | [6.854-AA](http://people.csail.mit.edu/moitra/854.html)      | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL6ogFv-ieghdoGKGg2Bik3Gl1glBTEu8c) | S2016      |
179 | | 12.  | **Introduction to Optimization**                             | Michael Zibulevsky, Technion                                 | `None`                                                       | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLBD31626529B0AC2A) | 2016       |
180 | | 13.  | **Convex Optimization**                                      | Javier Peña & Ryan Tibshirani                                | [10-725/36-725](https://www.stat.cmu.edu/~ryantibs/convexopt-F16) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLjbUi5mgii6AVdvImLB9-Hako68p9MpIC) | F2016      |
181 | | 14.  | **Convex Optimization**                                      | Ryan Tibshirani, CMU                                         | [10-725](https://www.stat.cmu.edu/~ryantibs/convexopt-F18/)  | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLpIxOj-HnDsMM7BCNGC3hPFU3DfCWfVIw) <br/> [Lecture-Videos](https://www.stat.cmu.edu/~ryantibs/convexopt-F18/) | F2018      |
182 | | 15.  | **Modern Algorithmic Optimization**                          | Yurii Nesterov, UCLouvain                                    | `None`                                                       | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLEqoHzpnmTfAoUDqnmMly-KgyJ6ZM_axf) | 2018       |
183 | | 16.  | **Optimization, Foundations of Optimization**                | Mark Walker, University of Arizona                           | [MathCamp-20](http://www.u.arizona.edu/~mwalker/MathCamp2020/MathCamp2020LectureNotes.htm) | [YouTube-Lectures-Found.](https://www.youtube.com/playlist?list=PLcjqUUQt__ZE6wp_c4-FcRdmzBvx8VN7O) <br/> [YouTube-Lectures-Opt](https://www.youtube.com/playlist?list=PLcjqUUQt__ZE0ZSTNRyBIgLJ5obPHdmxC) | 2019 - now |
184 | | 17.  | **Optimization: Principles and Algorithms**                  | Michel Bierlaire, École polytechnique fédérale de Lausanne (EPFL) | [opt-algo](https://transp-or.epfl.ch/books/optimization/html/about_book.html) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLM4Pv4KYYzGzOpWwsaV6GgllT6njsi1G-) | 2019       |
185 | | 18.  | **Optimization and Simulation**                              | Michel Bierlaire, École polytechnique fédérale de Lausanne (EPFL) | [opt-sim](https://transp-or.epfl.ch/courses/OptSim2019/slides.php) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL10NOnsbP5Q5NlJ-Y6Eiup6RTSfkuj1TR) | S2019      |
186 | | 19.  | **Brazilian Workshop on Continuous Optimization**            | Lots of Legends, Instituto Nacional de Matemática Pura e Aplicada, Rio de Janeiro | [cont. opt.](https://impa.br/eventos-do-impa/eventos-2019/xiii-brazilian-workshop-on-continuous-optimization) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLo4jXE-LdDTQVZhnLPq2W31vJ1fq1VSp6) | 2019       |
187 | | 20.  | **One World Optimization Seminar**                           | Lots of Legends, Universität Wien                            | [1W-OPT](https://owos.univie.ac.at)                          | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLBQo-yZOMzLWEcAptzTYOnwXo9hhXrAa2) | 2020-      |
188 | |      |                                                              |                                                              |                                                              |                                                              |            |
189 | | 21.  | **Convex Optimization II**                                   | Constantine Caramanis, UT Austin                             | [CVX-Optim-II](http://users.ece.utexas.edu/~cmcaram/constantine_caramanis/Announcements.html) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLXsmhnDvpjORzPelSDs0LSDrfJcqyLlZc) | S2020      |
190 | | 22.  | **Combinatorial Optimization**                               | Constantine Caramanis, UT Austin                             | [comb-op](https://caramanis.github.io/teaching/)             | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLXsmhnDvpjORcTRFMVF3aUgyYlHsxfhNL) | F2020      |
191 | | 23.  | **Optimization Methods for Machine Learning and Engineering** | Julius Pfrommer, Jürgen Beyerer, Karlsruher Institut für Technologie (KIT) | [Optim-MLE](https://ies.iar.kit.edu/lehre_1487.php), [slides](https://drive.google.com/drive/folders/1WWVWV4vDBIOkjZc6uFY3nfXvpaOUHcfb) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLdkTDauaUnQpzuOCZyUUZc0lxf4-PXNR5) | W2020-21   |
192 | |      |                                                              |                                                              |                                                              |                                                              |            |
193 | 
194 | [Go to Contents :arrow_heading_up:](https://github.com/kmario23/deep-learning-drizzle#contents) 
195 | 
196 | :heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign:
197 | ### :cupid: General Machine Learning :cyclone: :boom: 
198 | :heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign:
199 | 
200 | | S.No | Course Name                                                  | University/Instructor(s)                                     | Course Webpage                                               | Video Lectures                                               | Year      |
201 | | ---- | ------------------------------------------------------------ | ------------------------------------------------------------ | ------------------------------------------------------------ | ------------------------------------------------------------ | --------- |
202 | | 1.   | **CS229: Machine Learning**                                  | Andrew Ng, Stanford University                               | [CS229-old](https://see.stanford.edu/Course/CS229/) <br/> [CS229-new](http://cs229.stanford.edu/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLA89DCFA6ADACE599) | 2007      |
203 | | 2.   | **Machine Learning**                                         | Jeffrey Miller, Brown University                             | `mathematical monk`                                          | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLD0F06AA0D2E8FFBA) | 2011      |
204 | | 3.   | **Machine Learning**                                         | Tom Mitchell, CMU                                            | [10-701](http://www.cs.cmu.edu/~tom/10701_sp11/)             | [Lecture-Videos](http://www.cs.cmu.edu/~tom/10701_sp11/lectures.shtml) | 2011      |
205 | | 4.   | **Machine Learning and Data Mining**                         | Nando de Freitas, University of British Columbia             | [CPSC-340](https://www.cs.ubc.ca/~nando/340-2012/index.php)  | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLE6Wd9FR--Ecf_5nCbnSQMHqORpiChfJf) | 2012      |
206 | | 5.   | **Learning from Data**                                       | Yaser Abu-Mostafa, CalTech                                   | [CS156](http://work.caltech.edu/telecourse.html)             | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLD63A284B7615313A) | 2012      |
207 | | 6.   | **Machine Learning**                                         | Rudolph Triebel, Technische Universität München              | [Machine Learning](https://vision.in.tum.de/teaching/ws2013/ml_ws13) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLTBdjV_4f-EIiongKlS9OKrBEp8QR47Wl) | 2013      |
208 | | 7.   | **Introduction to Machine Learning**                         | Alex Smola, CMU                                              | [10-701](http://alex.smola.org/teaching/cmu2013-10-701/)     | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLZSO_6-bSqHQmMKwWVvYwKreGu4b4kMU9) | 2013      |
209 | | 8.   | **Introduction to Machine Learning**                         | Alex Smola and Geoffrey Gordon, CMU                          | [10-701x](http://alex.smola.org/teaching/cmu2013-10-701x/)   | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLZSO_6-bSqHR7NPk4k0zqdm2dPdraQZ_B) | 2013      |
210 | | 9.   | **Pattern Recognition**                                      | Sukhendu Das, IIT-M and C.A. Murthy, ISI-Calcutta            | [PR-NPTEL](https://nptel.ac.in/syllabus/106106046/)          | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLbMVogVj5nJQJMLb2CYw9rry0d5s0TQRp) | 2014      |
211 | | 10.  | **An Introduction to Statistical Learning with Applications in R** | Trevor Hastie and Robert Tibshirani, Stanford                | [stat-learn](https://lagunita.stanford.edu/courses/HumanitiesandScience/StatLearning/Winter2015/about) <br/> [R-bloggers](https://www.r-bloggers.com/in-depth-introduction-to-machine-learning-in-15-hours-of-expert-videos/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLOg0ngHtcqbPTlZzRHA2ocQZqB1D_qZ5V) | 2014      |
212 | |      |                                                              |                                                              |                                                              |                                                              |           |
213 | | 11.  | **Introduction to Machine Learning**                         | Katie Malone, Sebastian Thrun, Udacity                       | [ML-Udacity](https://www.udacity.com/course/ud120)           | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLAwxTw4SYaPkQXg8TkVdIvYv4HfLG7SiH) | 2015      |
214 | | 12.  | **Introduction to Machine Learning**                         | Dhruv Batra, Virginia Tech                                   | [ECE-5984](https://filebox.ece.vt.edu/~s15ece5984/)          | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL-fZD610i7yDUiNTFy-tEOxkTwg4mHZHu) | 2015      |
215 | | 13.  | **Statistical Learning - Classification**                    | Ali Ghodsi, University of Waterloo                           | [STAT-441](https://uwaterloo.ca/data-analytics/statistical-learning-classification) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLehuLRPyt1Hy-4ObWBK4Ab0xk97s6imfC) | 2015      |
216 | | 14.  | **Machine Learning Theory**                                  | Shai Ben-David, University of Waterloo                       | `None`                                                       | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLPW2keNyw-usgvmR7FTQ3ZRjfLs5jT4BO) | 2015      |
217 | | 15.  | **Introduction to Machine Learning**                         | Alex Smola, CMU                                              | [10-701](http://alex.smola.org/teaching/10-701-15/)          | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLZSO_6-bSqHTTV7w9u7grTXBHMH-mw3qn) | S2015     |
218 | | 16.  | **Statistical Machine Learning**                             | Larry Wasserman, CMU                                         | `None`                                                       | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLjbUi5mgii6BWEUZf7He6nowWvGne_Y8r) | S2015     |
219 | | 17.  | **ML: Supervised Learning**                                  | Michael Littman, Charles Isbell, Pushkar Kolhe, GaTech       | [ML-Udacity](https://eu.udacity.com/course/machine-learning--ud262) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLAwxTw4SYaPl0N6-e1GvyLp5-MUMUjOKo) | 2015      |
220 | | 18.  | **ML: Unsupervised Learning**                                | Michael Littman, Charles Isbell, Pushkar Kolhe, GaTech       | [ML-Udacity](https://eu.udacity.com/course/machine-learning-unsupervised-learning--ud741) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLAwxTw4SYaPmaHhu-Lz3mhLSj-YH-JnG7) | 2015      |
221 | | 19.  | **Advanced Introduction to Machine Learning**                | Barnabas Poczos and Alex Smola                               | [10-715](https://www.cs.cmu.edu/~bapoczos/Classes/ML10715_2015Fall/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL4YhK0pT0ZhWBzSBkMGzpnPw6sf6Ma0IX) | F2015     |
222 | | 20.  | **Machine Learning**                                         | Pedro Domingos, UWashington                                  | [CSEP-546](https://courses.cs.washington.edu/courses/csep546/16sp/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLTPQEx-31JXgtDaC6-3HxWcp7fq4N8YGr) | S2016     |
223 | |      |                                                              |                                                              |                                                              |                                                              |           |
224 | | 21.  | **Statistical Machine Learning**                             | Larry Wasserman, CMU                                         | `None`                                                       | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLTB9VQq8WiaCBK2XrtYn5t9uuPdsNm7YE) | S2016     |
225 | | 22.  | **Machine Learning with Large Datasets**                     | William Cohen, CMU                                           | [10-605](http://curtis.ml.cmu.edu/w/courses/index.php/Machine_Learning_with_Large_Datasets_10-605_in_Fall_2016) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLnfBqXRW5MRhPtfkadfwQ0VcuSi2IwEcW) | F2016     |
226 | | 23.  | **Math Background for Machine Learning**                     | Geoffrey Gordon, CMU                                         | `10-600`                                                     | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL7y-1rk2cCsA339crwXMWUaBRuLBvPBCg) | F2016     |
227 | | 24.  | **Statistical Learning - Classification**                    | Ali Ghodsi, University of Waterloo                           | `None`                                                       | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLehuLRPyt1HzXDemu7K4ETcF0Ld_B5adG) | 2017      |
228 | | 25.  | **Machine Learning**                                         | Andrew Ng, Stanford University                               | [Coursera-ML](https://www.coursera.org/learn/machine-learning) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLLssT5z_DsK-h9vYZkQkYNWcItqhlRJLN) | 2017      |
229 | | 26.  | **Machine Learning**                                         | Roni Rosenfield, CMU                                         | [10-601](http://www.cs.cmu.edu/~roni/10601-f17/)             | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL7k0r4t5c10-g7CWCnHfZOAxLaiNinChk) | 2017      |
230 | | 27.  | **Statistical Machine Learning**                             | Ryan Tibshirani, Larry Wasserman, CMU                        | [10-702](http://www.stat.cmu.edu/~ryantibs/statml/)          | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLjbUi5mgii6B7A0nM74zHTOVQtTC9DaCv) | S2017     |
231 | | 28.  | **Machine Learning for Computer Vision**                     | Fred Hamprecht, Heidelberg University                        | `None`                                                       | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLuRaSnb3n4kSQFyt8VBldsQ9pO9Xtu8rY) | F2017     |
232 | | 29.  | **Math Background for Machine Learning**                     | Geoffrey Gordon, CMU                                         | [10-606 / 10-607](https://canvas.cmu.edu/courses/603/assignments/syllabus) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL7y-1rk2cCsAqRtWoZ95z-GMcecVG5mzA) | F2017     |
233 | | 30.  | **Data Visualization**                                       | Ali Ghodsi, University of Waterloo                           | `None`                                                       | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLehuLRPyt1HzQoXEhtNuYTmd0aNQvtyAK) | 2017      |
234 | |      |                                                              |                                                              |                                                              |                                                              |           |
235 | | 31.  | **Machine Learning for Physicists**                          | Florian Marquardt, Uni Erlangen-Nürnberg                     | [ML4Phy-17](http://www.thp2.nat.uni-erlangen.de/index.php/2017_Machine_Learning_for_Physicists,_by_Florian_Marquardt) | [Lecture-Videos](https://www.video.uni-erlangen.de/course/id/574) | 2017      |
236 | | 32.  | **Machine Learning for Intelligent Systems**                 | Kilian Weinberger, Cornell University                        | [CS4780](http://www.cs.cornell.edu/courses/cs4780/2018fa/)   | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLl8OlHZGYOQ7bkVbuRthEsaLr7bONzbXS) | F2018     |
237 | | 33.  | **Statistical Learning Theory and Applications**             | Tomaso Poggio, Lorenzo Rosasco, Sasha Rakhlin                | [9.520/6.860](https://cbmm.mit.edu/lh-9-520)                 | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLyGKBDfnk-iAtLO6oLW4swMiQGz4f2OPY) | F2018     |
238 | | 34.  | **Machine Learning and Data Mining**                         | Mike Gelbart, University of British Columbia                 | [CPSC-340](https://ubc-cs.github.io/cpsc340/)                | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLWmXHcz_53Q02ZLeAxigki1JZFfCO6M-b) | 2018      |
239 | | 35.  | **Foundations of Machine Learning**                          | David Rosenberg, Bloomberg                                   | [FOML](https://bloomberg.github.io/foml/#home)               | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLnZuxOufsXnvftwTB1HL6mel1V32w0ThI) | 2018      |
240 | | 36.  | **Introduction to Machine Learning**                         | Andreas Krause, ETH Zürich                                   | [IntroML](https://las.inf.ethz.ch/teaching/introml-s18)      | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLzn6LN6WhlN273tsqyfdrBUsA-o5nUESV) | 2018      |
241 | | 37.  | **Machine Learning Fundamentals**                            | Sanjoy Dasgupta, UC-San Diego                                | [MLF-slides](https://drive.google.com/drive/folders/1l1rwv-jMihLZIpW0zTgGN9-snWOsA3M9) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL_onPhFCkVQhUzcTVgQiC8W2ShZKWlm0s) | 2018      |
242 | | 38.  | **Machine Learning**                                         | Jordan Boyd-Graber, University of Maryland                   | [CMSC-726](http://users.umiacs.umd.edu/~jbg/teaching/CMSC_726/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLegWUnz91WfsELyRcZ7d1GwAVifDaZmgo) | 2015-2018 |
243 | | 39.  | **Machine Learning**                                         | Andrew Ng, Stanford University                               | [CS229](http://cs229.stanford.edu/syllabus-autumn2018.html)  | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLoROMvodv4rMiGQp3WXShtMGgzqpfVfbU) | 2018      |
244 | | 40.  | **Machine Intelligence**                                     | H.R.Tizhoosh, UWaterloo                                      | [SYDE-522](https://kimialab.uwaterloo.ca/kimia/index.php/teaching/syde-522-machine-intelligence-2) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL4upCU5bnihwCX93Gv6AQnKmVMwx4AZoT) | 2019      |
245 | |      |                                                              |                                                              |                                                              |                                                              |           |
246 | | 41.  | **Introduction to Machine Learning**                         | Pascal Poupart, University of Waterloo                       | [CS480/680](https://cs.uwaterloo.ca/~ppoupart/teaching/cs480-spring19) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLdAoL1zKcqTW-uzoSVBNEecKHsnug_M0k) | S2019     |
247 | | 42.  | **Advanced Machine Learning**                                | Thorsten Joachims, Cornell University                        | [CS-6780](https://www.cs.cornell.edu/courses/cs6780/2019sp)  | [Lecture-Videos](https://cornell.mediasite.com/Mediasite/Catalog/Full/f5d1cd3323f746cca80b2468bf97efd421) | S2019     |
248 | | 43.  | **Machine Learning for Structured Data**                     | Matt Gormley, Carnegie Mellon University                     | [10-418/10-618](http://www.cs.cmu.edu/~mgormley/courses/10418/schedule.html) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL4CxkUJbvNVihRKP4bXufvRLIWzeS-ieP) | F2019     |
249 | | 44.  | **Advanced Machine Learning**                                | Joachim Buhmann, ETH Zürich                                  | [ML2-AML](https://ml2.inf.ethz.ch/courses/aml/)              | [Lecture-Videos](https://video.ethz.ch/lectures/d-infk/2019/autumn/252-0535-00L.html) | F2019     |
250 | | 45.  | **Machine Learning for Signal Processing**                   | Vipul Arora, IIT-Kanpur                                      | [MLSP](http://home.iitk.ac.in/~vipular/stuff/2019_MLSP.html) | [Lecture-Videos](https://iitk-my.sharepoint.com/:f:/g/personal/vipular_iitk_ac_in/Enf97NZfsoVBiyclC6yHfe4BlUv6CA4U8LPQQ4vtsDo_Xg) | F2019     |
251 | | 46.  | **Foundations of Machine Learning**                          | Animashree Anandkumar, CalTech                               | [CMS-165](http://tensorlab.cms.caltech.edu/users/anima/cms165-2019.html) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLVNifWxslHCA5GUh0o92neMiWiQiGVFqp) | 2019      |
252 | | 47.  | **Machine Learning for Physicists**                          | Florian Marquardt, Uni Erlangen-Nürnberg                     | `None`                                                       | [Lecture-Videos](https://www.video.uni-erlangen.de/course/id/778) | 2019      |
253 | | 48.  | **Applied Machine Learning**                                 | Andreas Müller, Columbia University                          | [COMS-W4995](https://www.cs.columbia.edu/~amueller/comsw4995s19/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL_pVmAaAnxIQGzQS2oI3OWEPT-dpmwTfA) | 2019      |
254 | | 49.  | **Fundamentals of Machine Learning over Networks**           | Hossein Shokri-Ghadikolaei, KTH, Sweden                      | [MLoNs](https://sites.google.com/view/mlons/course-materials) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLWoZTd81WFCEBFrxDfNUrDnt3ABdLfg80) | 2019      |
255 | | 50.  | **Foundations of Machine Learning and Statistical Inference** | Animashree Anandkumar, CalTech                               | [CMS-165](http://tensorlab.cms.caltech.edu/users/anima/cms165-2020.html) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLVNifWxslHCDlbyitaLLYBOAEPbmF1AHg) | 2020      |
256 | |      |                                                              |                                                              |                                                              |                                                              |           |
257 | | 51.  | **Machine Learning**                                         | Rebecca Willett and Yuxin Chen, University of Chicago        | [STAT 37710 / CMSC 35400](https://voices.uchicago.edu/willett/teaching/stats37710-cmsc35400-s20) | [Lecture-Videos](https://voices.uchicago.edu/willett/teaching/stats37710-cmsc35400-s20) | S2020     |
258 | | 52.  | **Introduction to Machine Learning**                         | Sanjay Lall and Stephen Boyd, Stanford University            | [EE104/CME107](http://ee104.stanford.edu)                    | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLoROMvodv4rN_Uy7_wmS051_q1d6akXmK) | S2020     |
259 | | 53.  | **Applied Machine Learning**                                 | Andreas Müller, Columbia University                          | [COMS-W4995](https://www.cs.columbia.edu/~amueller/comsw4995s20/schedule/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL_pVmAaAnxIRnSw6wiCpSvshFyCREZmlM) | S2020     |
260 | | 54.  | **Statistical Machine Learning**                             | Ulrike von Luxburg, Eberhard Karls Universität Tübingen      | [Stat-ML](https://www.tml.cs.uni-tuebingen.de/teaching/2020_statistical_learning/index.php) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL05umP7R6ij2XCvrRzLokX6EoHWaGA2cC) | SS2020    |
261 | | 55.  | **Probabilistic Machine Learning**                           | Philipp Hennig, Eberhard Karls Universität Tübingen          | [Prob-ML](https://uni-tuebingen.de/en/180804)                | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL05umP7R6ij1tHaOFY96m5uX3J21a6yNd) | SS2020    |
262 | | 56.  | **Machine Learning**                                         | Sarath Chandar, PolyMTL, UdeM, Mila                          | [INF8953CE](http://sarathchandar.in/teaching/ml/fall2020)    | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLImtCgowF_ET0mi-AmmqQ0SIJUpWYaIOr) | F2020     |
263 | | 57.  | **Machine Learning**                                         | Erik Bekkers, Universiteit van Amsterdam                     | [UvA-ML](https://uvaml1.github.io/)                          | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL8FnQMH2k7jzhtVYbKmvrMyXDYMmgjj_n) | F2020     |
264 | | 58.  | **Neural Networks for Signal Processing**                    | Shayan Srinivasa Garani, Indian Institute of Science         | [NN4SP](https://labs.dese.iisc.ac.in/pnsil/neural-networks-and-learning-systems-i-fall-2020/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLgMDNELGJ1CZn1399dV7_U4VBNJflRsua) | F2020     |
265 | | 59.  | **Introduction to Machine Learning**                         | Dmitry Kobak, Universität Klinikum Tübingen                  | `None`                                                       | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL05umP7R6ij35ShKLDqccJSDntugY4FQT) | 2020      |
266 | | 60.  | **Machine Learning (PRML)**                                  | Erik J. Bekkers, Universiteit van Amsterdam                  | [UvAML-1](https://uvaml1.github.io)                          | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL8FnQMH2k7jzhtVYbKmvrMyXDYMmgjj_n) | 2020      |
267 | |      |                                                              |                                                              |                                                              |                                                              |           |
268 | | 61.  | **Machine Learning with Kernel Methods**                     | Julien Mairal and Jean-Philippe Vert, Inria/ENS Paris-Saclay, Google | [ML-Kernels](http://members.cbio.mines-paristech.fr/~jvert/svn/kernelcourse/course/2021mva/index.html) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLD93kGj6_EdrkNj27AZMecbRlQ1SMkp_o) | S2021     |
269 | | 62.  | **Continual Learning**                                       | Vincenzo Lomonaco, Università di Pisa                        | [ContLearn'21](https://course.continualai.org/background/details) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLm6QXeaB-XkBfM5RgQP6wCR7Jegdg51Px) | 2021      |
270 | | 63.  | **Causality**                                                | Christina Heinze-Deml, ETH Zurich                            | [Causal'21](https://stat.ethz.ch/lectures/ss21/causality.php#course_materials) | [YouTube-Lectures](https://stat.ethz.ch/lectures/ss21/causality.php#course_materials) | 2021      |
271 | |      |                                                              |                                                              |                                                              |                                                              |           |
272 | 
273 | [Go to Contents :arrow_heading_up:](https://github.com/kmario23/deep-learning-drizzle#contents) 
274 | 
275 | :heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign:
276 | ### :balloon: Reinforcement Learning :hotsprings: :video_game: 
277 | :heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign:
278 | 
279 | | S.No | Course Name                                              | University/Instructor(s)                                     | Course Webpage                                               | Video Lectures                                               | Year   |
280 | | ---- | -------------------------------------------------------- | ------------------------------------------------------------ | ------------------------------------------------------------ | ------------------------------------------------------------ | ------ |
281 | | 1.   | **A Short Course on Reinforcement Learning**             | Satinder Singh, UMichigan                                    | `None`                                                       | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLM4Pv4KYYzGy4cIFQ5C36-1jMNLab80Ky) | 2011   |
282 | | 2.   | **Approximate Dynamic Programming**                      | Dimitri P. Bertsekas, MIT                                    | [Lecture-Slides](http://adpthu2014.weebly.com/slides--materials.html) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLiCLbsFQNFAxOmVeqPhI5er1LGf2-L9I4) | 2014   |
283 | | 3.   | **Introduction to Reinforcement Learning**               | David Silver, DeepMind                                       | [UCL-RL](http://www0.cs.ucl.ac.uk/staff/d.silver/web/Teaching.html) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLqYmG7hTraZDM-OYHWgPebj2MfCFzFObQ) | 2015   |
284 | | 4.   | **Reinforcement Learning**                               | Charles Isbell, Chris Pryby, GaTech; Michael Littman, Brown  | [RL-Udacity](https://eu.udacity.com/course/reinforcement-learning--ud600) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLAwxTw4SYaPnidDwo9e2c7ixIsu_pdSNp) | 2015   |
285 | | 5.   | **Reinforcement Learning**                               | Balaraman Ravindran, IIT Madras                              | [RL-IITM](https://www.cse.iitm.ac.in/~ravi/courses/Reinforcement%20Learning.html) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLNdWVHi37UggQIVcaZcmtGGEQHY9W7d9D) | 2016   |
286 | | 6.   | **Deep Reinforcement Learning**                          | Sergey Levine, UC Berkeley                                   | [CS-294](http://rail.eecs.berkeley.edu/deeprlcoursesp17/)    | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLkFD6_40KJIwTmSbCv9OVJB3YaO4sFwkX) | S2017  |
287 | | 7.   | **Deep Reinforcement Learning**                          | Sergey Levine, UC Berkeley                                   | [CS-294](http://rail.eecs.berkeley.edu/deeprlcourse-fa17/)   | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLkFD6_40KJIznC9CDbVTjAF2oyt8_VAe3) | F2017  |
288 | | 8.   | **Deep RL Bootcamp**                                     | Many legends, UC Berkeley                                    | [Deep-RL](https://sites.google.com/view/deep-rl-bootcamp/lectures) | [YouTube-Lectures](https://www.youtube.com/channel/UCTgM-VlXKuylPrZ_YGAJHOw/videos) | 2017   |
289 | | 9    | **Data Efficient Reinforcement Learning**                | Lots of Legends, Canary Islands                              | [DERL-17](http://dalimeeting.org/dali2017/data-efficient-reinforcement-learning.html) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL-tWvTpyd1VAvDpxukup6w-SuZQQ7e8K8) | 2017   |
290 | | 10.  | **Deep Reinforcement Learning**                          | Sergey Levine, UC Berkeley                                   | [CS-294-112](http://rail.eecs.berkeley.edu/deeprlcourse-fa18/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLkFD6_40KJIxJMR-j5A1mkxK26gh_qg37) | 2018   |
291 | |      |                                                          |                                                              |                                                              |                                                              |        |
292 | | 11.  | **Reinforcement Learning**                               | Pascal Poupart, University of Waterloo                       | [CS-885](https://cs.uwaterloo.ca/~ppoupart/teaching/cs885-spring18/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLdAoL1zKcqTXFJniO3Tqqn6xMBBL07EDc) | 2018   |
293 | | 12.  | **Deep Reinforcement Learning and Control**              | Katerina Fragkiadaki and Tom Mitchell, CMU                   | [10-703](http://www.andrew.cmu.edu/course/10-703/)           | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLpIxOj-HnDsNfvOwRKLsUobmnF2J1l5oV) | 2018   |
294 | | 13.  | **Reinforcement Learning and Optimal Control**           | Dimitri Bertsekas, Arizona State University                  | [RLOC](http://web.mit.edu/dimitrib/www/RLbook.html)          | [Lecture-Videos](http://web.mit.edu/dimitrib/www/RLbook.html) | 2019   |
295 | | 14.  | **Reinforcement Learning**                               | Emma Brunskill, Stanford University                          | [CS 234](http://web.stanford.edu/class/cs234/index.html)     | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLoROMvodv4rOSOPzutgyCTapiGlY2Nd8u) | 2019   |
296 | | 15.  | **Reinforcement Learning Day**                           | Lots of Legends, Microsoft Research, New York                | [RLD-19](https://www.microsoft.com/en-us/research/event/reinforcement-learning-day-2019/#!agenda) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLD7HFcN7LXRe9nWEX3Up-RiCDi6-0mqVC) | 2019   |
297 | | 16.  | **New Directions in Reinforcement Learning and Control** | Lots of Legends, IAS, Princeton University                   | [NDRLC-19](https://www.math.ias.edu/ndrlc)                   | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLdDZb3TwJPZ61sGqd6cbWCmTc275NrKu3) | 2019   |
298 | | 17.  | **Deep Reinforcement Learning**                          | Sergey Levine, UC Berkeley                                   | [CS 285](http://rail.eecs.berkeley.edu/deeprlcourse-fa19)    | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLkFD6_40KJIwhWJpGazJ9VSj9CFMkb79A) | F2019  |
299 | | 18.  | **Deep Multi-Task and Meta Learning**                    | Chelsea Finn, Stanford University                            | [CS 330](https://cs330.stanford.edu/)                        | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLoROMvodv4rMC6zfYmnD7UG3LVvwaITY5) | F2019  |
300 | | 19.  | **RL-Theory Seminars**                                   | Lots of Legends, Earth                                       | [RL-theory-sem](https://sites.google.com/view/rltheoryseminars/past-seminars) | [YouTube-Lectures](https://www.youtube.com/channel/UCfBFutC9RbKK6p--B4R9ebA/videos) | 2020 - |
301 | | 20.  | **Deep Reinforcement Learning**                          | Sergey Levine, UC Berkeley                                   | [CS 285](http://rail.eecs.berkeley.edu/deeprlcourse-fa20)    | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL_iWQOsE6TfURIIhCrlt-wj9ByIVpbfGc) | F2020  |
302 | |      |                                                          |                                                              |                                                              |                                                              |        |
303 | | 21.  | **Introduction to Reinforcement Learning**               | Amir-massoud Farahmand, Vector Institute, University of Toronto | [RL-intro](https://amfarahmand.github.io/IntroRL)            | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLCveiXxL2xNbiDq51a8iJwPRq2aO0ykrq) | S2021  |
304 | | 22.  | **Reinforcement Learning**                               | Antonio Celani and Emanuele Panizon, International Centre for Theoretical Physics | `None`                                                       | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLp0hSY2uBeP8q2G3mfHGVGvQFEMX0QRWM) | 2021   |
305 | | 23.  | **Computational Sensorimotor Learning**                  | Pulkit Agrawal, MIT-CSAIL                                    | [6.884-CSL](https://pulkitag.github.io/6.884/lectures)       | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLwNwxAG-kBxPMTIs2fKWSsf7HqL2TcC78) | S2021  |
306 | | 24.  | **Reinforcement Learning**                               | Dimitri P. Bertsekas, ASU/MIT                                | [RL-21](http://web.mit.edu/dimitrib/www/RLbook.html)         | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLmH30BG15SIp79JRJ-MVF12uvB1qPtPzn) | S2021  |
307 | | 25.  | **Reinforcement Learning**                               | Sarath Chandar,  École Polytechnique de Montréal             | [INF8953DE](https://chandar-lab.github.io/INF8953DE)         | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLImtCgowF_ES_JdF_UcM60EXTcGZg67Ua) | F2021  |
308 | | 26.  | **Deep Reinforcement Learning**                          | Sergey Levine, UC Berkeley                                   | [CS 285](http://rail.eecs.berkeley.edu/deeprlcourse)         | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL_iWQOsE6TfXxKgI1GgyV1B_Xa0DxE5eH) | F2021  |
309 | | 27.  | **Reinforcement Learning Lecture Series**                | Lots of Legends, DeepMind & UC London                        | [RL-series](https://deepmind.com/learning-resources/reinforcement-learning-series-2021) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLqYmG7hTraZDVH599EItlEWsUOsJbAodm) | 2021   |
310 | | 28.  | **Reinforcement Learning**                               | Dimitri P. Bertsekas, ASU/MIT                                | [RL-22](http://web.mit.edu/dimitrib/www/RLbook.html)         | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLmH30BG15SIoXhxLldoio0BhsIY84YMDj) | S2022  |
311 | |      |                                                          |                                                              |                                                              |                                                              |        |
312 | 
313 | [Go to Contents :arrow_heading_up:](https://github.com/kmario23/deep-learning-drizzle#contents) 
314 | 
315 | :heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign:
316 | ### :loudspeaker: Probabilistic Graphical Models :sparkles: 
317 | :heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign:
318 | 
319 | | S.No | Course Name                                                  | University/Instructor(s)                            | Course WebPage                                               | Lecture Videos                                               | Year    |
320 | | ---- | ------------------------------------------------------------ | --------------------------------------------------- | ------------------------------------------------------------ | ------------------------------------------------------------ | ------- |
321 | | 1.   | **Probabilistic Graphical Models**                           | Many Legends, MPI-IS                                | [MLSS-Tuebingen](http://mlss.tuebingen.mpg.de/2013/2013/speakers.html) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLL0GjJzXhAWTRiW_ynFswMaiLSa0hjCZ3) | 2013    |
322 | | 2.   | **Probabilistic Modeling and Machine Learning**              | Zoubin Ghahramani, University of Cambridge          | [WUST-Wroclaw](https://www.ii.pwr.edu.pl/~gonczarek/zoubin.html) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLwUOK5j_XOsdfVAGKErx9HqnrVZIuRbZ2) | 2013    |
323 | | 3.   | **Probabilistic Graphical Models**                           | Eric Xing, CMU                                      | [10-708](http://www.cs.cmu.edu/~epxing/Class/10708/lecture.html) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLI3nIOD-p5aoXrOzTd1P6CcLavu9rNtC-) | 2014    |
324 | | 4.   | **Learning with Structured Data: An Introduction to Probabilistic Graphical Models** | Christoph Lampert, IST Austria                      | `None`                                                       | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLEqoHzpnmTfA0wc1JxjoVVOrJlx8W0rGf) | 2016    |
325 | | 5.   | **Probabilistic Graphical Models**                           | Nicholas Zabaras, University of Notre Dame          | [PGM](https://www.zabaras.com/probabilistic-graphical-models) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLd-PuDzW85AcV4bgdu7wHPL37hm60W4RM) | 2018    |
326 | | 6.   | **Probabilistic Graphical Models**                           | Eric Xing, CMU                                      | [10-708](https://sailinglab.github.io/pgm-spring-2019/)      | [Lecture-Videos](https://sailinglab.github.io/pgm-spring-2019/lectures) <br> [YouTube-Lectures](https://www.youtube.com/playlist?list=PLoZgVqqHOumTY2CAQHL45tQp6kmDnDcqn) | S2019   |
327 | | 7.   | **Probabilistic Graphical Models**                           | Eric Xing, CMU                                      | [10-708](https://www.cs.cmu.edu/~epxing/Class/10708-20/index.html) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLoZgVqqHOumTqxIhcdcpOAJOOimrRCGZn) | S2020   |
328 | | 8.   | **Uncertainty Modeling in AI**                               | Gim Hee Lee, National University of Singapura (NUS) | [CS 5340 - CH](https://www.coursehero.com/sitemap/schools/2652-National-University-of-Singapore/courses/7821096-CS5340/), [CS 5340-NB](https://github.com/clear-nus/CS5340-notebooks) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLxg0CGqViygOb9Eyc8IXM27doxjp2SK0H) | 2020-21 |
329 | |      |                                                              |                                                     |                                                              |                                                              |         |
330 | 
331 | [Go to Contents :arrow_heading_up:](https://github.com/kmario23/deep-learning-drizzle#contents)
332 | 
333 | :heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign:
334 | ## :game_die: Bayesian Deep Learning :spades: :gem: 
335 | :heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign:
336 | 
337 | | S.No | Course Name                                         | University/Instructor(s)          | Course WebPage                                           | Lecture Videos                                               | Year     |
338 | | ---- | --------------------------------------------------- | --------------------------------- | -------------------------------------------------------- | ------------------------------------------------------------ | -------- |
339 | | 1.   | **Bayesian Neural Networks, Variational Inference** | Lots of Legends                   | `None`                                                   | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLM4Pv4KYYzGwUB4bFy183hwGhpL9ytvA1) | 2014-now |
340 | | 2.   | **Variational Inference**                           | Chieh Wu, Northeastern University | `None`                                                   | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLdk2fd27CQzSd1sQ3kBYL4vtv6GjXvPsE) | 2015     |
341 | | 3.   | **Deep Learning and Bayesian Methods**              | Lots of Legends, HSE Moscow       | [DLBM-SS](http://deepbayes.ru/2018)                      | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLe5rNUydzV9Q01vWCP9BV7NhJG3j7mz62) | 2018     |
342 | | 4.   | **Deep Learning and Bayesian Methods**              | Lots of Legends, HSE Moscow       | [DLBM-SS](http://deepbayes.ru/)                          | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLe5rNUydzV9QHe8VDStpU0o8Yp63OecdW) | 2019     |
343 | | 5.   | **Nordic Probabilistic AI**                         | Lots of Legends, NTNU, Trondheim  | [ProbAI](https://github.com/probabilisticai/probai-2019) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLRy-VW__9hV8s--JkHXZvnd26KgjRP2ik) | 2019     |
344 | |      |                                                     |                                   |                                                          |                                                              |          |
345 | 
346 | [Go to Contents :arrow_heading_up:](https://github.com/kmario23/deep-learning-drizzle#contents)
347 | 
348 | :heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign:
349 | ## :movie_camera: Medical Imaging :camera: :video_camera: 
350 | :heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign:
351 | 
352 | | S.No | Course Name                                                  | University/Instructor(s)                    | Course WebPage                                               | Lecture Videos                                               | Year  |
353 | | ---- | ------------------------------------------------------------ | ------------------------------------------- | ------------------------------------------------------------ | ------------------------------------------------------------ | ----- |
354 | | 1.   | **Medical Imaging Summer School**                            | Lots of Legends, Sicily                     | [MISS-14](http://iplab.dmi.unict.it/miss14/programme.html)   | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL_VeUGLULXQtvcCdAgmvKoJ1k0Ajhz-Qu) | 2014  |
355 | | 2.   | **Biomedical Image Analysis Summer School**                  | Lots of Legends, Paris                      | `None`                                                       | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLgSHH6boFf5uJAUT4ZRiAZc_ofXolkAGK) | 2015  |
356 | | 3.   | **Medical Imaging Summer School**                            | Lots of Legends, Sicily                     | [MISS-16](http://iplab.dmi.unict.it/miss16/programme.html)   | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLTRCr47yTx5iXIYSneX3LKf16upaw59wa) | 2016  |
357 | | 4.   | **OPtical and UltraSound imaging - OPUS**                    | Lots of Legends, Université de Lyon, France | [OPUS'16](https://opus2016lyon.sciencesconf.org/resource/page/id/2) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL95ayoVLX8GdUKbxu-R9WqRWwzdWcKjti) | 2016  |
358 | | 5.   | **Medical Imaging Summer School**                            | Lots of Legends, Sicily                     | [MISS-18](http://iplab.dmi.unict.it/miss/programme.htm)      | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL_VeUGLULXQux1dV4iA3XuMX6AueJmGGa) | 2018  |
359 | | 6.   | **Seminar on AI in Healthcare**                              | Lots of Legends, Stanford                   | [CS 522](http://cs522.stanford.edu/2018/index.html)          | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLYn-ZmPR1DtNQJ-ot-L2V2EgUEH6OH_7w) | 2018  |
360 | | 7.   | **Machine Learning for Healthcare**                          | David Sontag, Peter Szolovits, CSAIL MIT    | [MLHC-19](https://mlhc19mit.github.io/) <br/>[MIT 6.S897](https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-s897-machine-learning-for-healthcare-spring-2019/lecture-notes/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLUl4u3cNGP60B0PQXVQyGNdCyCTDU1Q5j) | S2019 |
361 | | 8.   | **Deep Learning and Medical Applications**                   | Lots of Legends, IPAM, UCLA                 | [DLM-20](https://www.ipam.ucla.edu/programs/workshops/deep-learning-and-medical-applications/?tab=schedule) | [Lecture-Videos](https://www.ipam.ucla.edu/programs/workshops/deep-learning-and-medical-applications/?tab=schedule) | 2020  |
362 | | 9.   | **Stanford Symposium on Artificial Intelligence in Medicine and Imaging** | Lots of Legends, Stanford AIMI              | [AIMI-20](https://aimi.stanford.edu/news-events/aimi-symposium/agenda) | [YouTube-Lectures](https://www.youtube.com/watch?v=tR2ObiL4il8&list=PLe6zdIMe5B7IR0oDOobXBDBlYY1eqLYPx) | 2020  |
363 | |      |                                                              |                                             |                                                              |                                                              |       |
364 | 
365 | [Go to Contents :arrow_heading_up:](https://github.com/kmario23/deep-learning-drizzle#contents)
366 | 
367 | :heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign:
368 | ## :tada: Graph Neural Networks (Geometric DL) :confetti_ball: :balloon: 
369 | :heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign:
370 | 
371 | | S.No | Course Name                                                  | University/Instructor(s)                                | Course WebPage                                               | Lecture Videos                                               | Year  |
372 | | ---- | ------------------------------------------------------------ | ------------------------------------------------------- | ------------------------------------------------------------ | ------------------------------------------------------------ | ----- |
373 | | 1.   | **Deep learning on graphs and manifolds**                    | Michael Bronstein, Technion                             | `None`                                                       | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLH39kM3nuavcVOUIIBraBNHjv-CwEd1uV) | 2017  |
374 | | 2.   | **Geometric Deep Learning on Graphs and Manifolds**          | Michael Bronstein, Technische Universität München       | `None`                                                       | [Lec-part1](https://streams.tum.de/Mediasite/Play/1f3b894e78f6400daa7885c886b936fb1d),  <br/>[Lec-part2](https://streams.tum.de/Mediasite/Play/6039c846b2f84e7a806024c06e3f5c5c1d) | 2017  |
375 | | 3.   | **Eurographics Symposium on Geometry Processing - Graduate School** | Lots of Legends, SIGGRAPH, London                       | [SGP-2017](http://geometry.cs.ucl.ac.uk/SGP2017/?p=gradschool) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLOp-ngXvomHArqntgLVNzuJNdzNx3rDjZ) | 2017  |
376 | | 4.   | **Eurographics Symposium on Geometry Processing - Graduate School** | Lots of Legends, SIGGRAPH, Paris                        | [SGP-2018](https://sgp2018.sciencesconf.org/resource/page/id/7) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLvcoRb-DvAmgpp8LYw7dUvLxh-1Vrrm-v) | 2018  |
377 | | 5.   | **Analysis of Networks: Mining and Learning with Graphs**    | Jure Leskovec, Stanford University                      | [CS224W](http://snap.stanford.edu/class/cs224w-2018/)        | [Lecture-Videos](http://snap.stanford.edu/class/cs224w-2018/) | 2018  |
378 | | 6.   | **Machine Learning with Graphs**                             | Jure Leskovec, Stanford University                      | [CS224W](http://snap.stanford.edu/class/cs224w-2019/)        | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL-Y8zK4dwCrQyASidb2mjj_itW2-YYx6-) | 2019  |
379 | | 7.   | Geometry and Learning from Data in 3D and Beyond -**Geometry and Learning from Data Tutorials** | Lots of Legends, IPAM UCLA                              | [GLDT](http://www.ipam.ucla.edu/programs/workshops/geometry-and-learning-from-data-tutorials) | [Lecture-Videos](http://www.ipam.ucla.edu/programs/workshops/geometry-and-learning-from-data-tutorials/?tab=schedule) | 2019  |
380 | | 8.   | Geometry and Learning from Data in 3D and Beyond - **Geometric Processing** | Lots of Legends, IPAM UCLA                              | [GeoPro](http://www.ipam.ucla.edu/programs/workshops/workshop-i-geometric-processing/) | [Lecture-Videos](http://www.ipam.ucla.edu/programs/workshops/workshop-i-geometric-processing/?tab=schedule) | 2019  |
381 | | 9.   | Geometry and Learning from Data in 3D and Beyond - **Shape Analysis** | Lots of Legends, IPAM UCLA                              | [Shape-Analysis](http://www.ipam.ucla.edu/programs/workshops/workshop-ii-shape-analysis/) | [Lecture-Videos](http://www.ipam.ucla.edu/programs/workshops/workshop-ii-shape-analysis/?tab=schedule) | 2019  |
382 | | 10.  | Geometry and Learning from Data in 3D and Beyond - **Geometry of Big Data** | Lots of Legends, IPAM UCLA                              | [Geo-BData](http://www.ipam.ucla.edu/programs/workshops/workshop-iii-geometry-of-big-data) | [Lecture-Videos](http://www.ipam.ucla.edu/programs/workshops/workshop-iii-geometry-of-big-data/?tab=schedule) | 2019  |
383 | |      |                                                              |                                                         |                                                              |                                                              |       |
384 | | 11.  | Geometry and Learning from Data in 3D and Beyond - **Deep Geometric Learning of Big Data and Applications** | Lots of Legends, IPAM UCLA                              | [DGL-BData](http://www.ipam.ucla.edu/programs/workshops/workshop-iv-deep-geometric-learning-of-big-data-and-applications) | [Lecture-Videos](http://www.ipam.ucla.edu/programs/workshops/workshop-iv-deep-geometric-learning-of-big-data-and-applications/?tab=schedule) | 2019  |
385 | | 12.  | **Israeli Geometric Deep Learning**                          | Lots of Legends, Israel                                 | [iGDL-20](https://gdl-israel.github.io/schedule.html)        | [Lecture-Videos](https://www.youtube.com/watch?v=c8_32IVn-sg) | 2020  |
386 | | 13.  | **Machine Learning for Graphs and Sequential Data**          | Stephan Günnemann, Technische Universität München (TUM) | [MLGS-20](https://www.in.tum.de/en/daml/teaching/summer-term-2020/machine-learning-for-graphs-and-sequential-data/) | [Lecture-Videos](https://www.in.tum.de/daml/teaching/mlgs/)  | S2020 |
387 | | 14.  | **Machine Learning with Graphs**                             | Jure Leskovec, Stanford                                 | [CS224W](http://web.stanford.edu/class/cs224w)               | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLoROMvodv4rPLKxIpqhjhPgdQy7imNkDn) | W2021 |
388 | | 15.  | **Geometric Deep Learning** - AMMI                           | Lots of Legends, Virtual                                | [GDL-AMMI](https://geometricdeeplearning.com/lectures)       | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLn2-dEmQeTfQ8YVuHBOvAhUlnIPYxkeu3) | 2021  |
389 | | 16.  | **Summer School on Geometric Deep Learning** -               | Lots of Legends, DTU, DIKU & AAU                        | [GDL- DTU, DIKU & AAU](https://geometric-deep-learning.compute.dtu.dk) | [Lecture-Videos](https://geometric-deep-learning.compute.dtu.dk/talks-and-materials) | 2021  |
390 | | 17.  | **Graph Neural Networks**                                    | Alejandro Ribeiro, University of Pennsylvania           | [ESE 514](https://gnn.seas.upenn.edu)                        | [YouTube-Lectures](https://www.youtube.com/channel/UC_YPrqpiEqkeGOG1TCt0giQ/playlists) | F2021 |
391 | |      |                                                              |                                                         |                                                              |                                                              |       |
392 | 
393 | [Go to Contents :arrow_heading_up:](https://github.com/kmario23/deep-learning-drizzle#contents)
394 | 
395 | :heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign:
396 | ### :hibiscus: Natural Language Processing :cherry_blossom: :sparkling_heart: 
397 | :heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign:
398 | 
399 | | S.No | Course Name                                         | University/Instructor(s)                                     | Course WebPage                                               | Lecture Videos                                               | Year      |
400 | | ---- | --------------------------------------------------- | ------------------------------------------------------------ | ------------------------------------------------------------ | ------------------------------------------------------------ | --------- |
401 | | 1.   | **Computational Linguistics I**                     | Jordan Boyd-Graber, University of Maryland                   | [CMS-723](http://users.umiacs.umd.edu/~jbg/teaching/CMSC_723/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLegWUnz91WfuPebLI97-WueAP90JO-15i) | 2013-2018 |
402 | | 2.   | **Deep Learning for Natural Language Processing**   | Nils Reimers, TU Darmstadt                                   | [DL4NLP](https://github.com/UKPLab/deeplearning4nlp-tutorial) | [YouTube-Lectures](https://www.youtube.com/channel/UC1zCuTrfpjT6Sv2kJk-JkvA/videos) | 2015-2017 |
403 | | 3.   | **Deep Learning for Natural Language Processing**   | Many Legends, DeepMind-Oxford                                | [DL-NLP](http://www.cs.ox.ac.uk/teaching/courses/2016-2017/dl/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL613dYIGMXoZBtZhbyiBqb0QtgK6oJbpm) | 2017      |
404 | | 4.   | **Deep Learning for Speech & Language**             | UPC Barcelona                                                | [DL-SL](https://telecombcn-dl.github.io/2017-dlsl/)          | [Lecture-Videos](https://telecombcn-dl.github.io/2017-dlsl/) | 2017      |
405 | | 5.   | **Neural Networks for Natural Language Processing** | Graham Neubig, CMU                                           | [NN4NLP](http://www.phontron.com/class/nn4nlp2017/)   [Code](https://github.com/neubig/nn4nlp-code) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL8PYTP1V4I8ABXzdqtOpB_eqBlVAz_xPT) | 2017      |
406 | | 6.   | **Neural Networks for Natural Language Processing** | Graham Neubig, CMU                                           | [NN4-NLP](http://www.phontron.com/class/nn4nlp2018/)         | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL8PYTP1V4I8Ba7-rY4FoB4-jfuJ7VDKEE) | 2018      |
407 | | 7.   | **Deep Learning for NLP**                           | Min-Yen Kan, NUS                                             | [CS-6101](https://www.comp.nus.edu.sg/~kanmy/courses/6101_1810/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLllwxvcS7ca5eD44KTCiT7Rmu_hFAafXB) | 2018      |
408 | | 8.   | **Neural Networks for Natural Language Processing** | Graham Neubig, CMU                                           | [NN4NLP](http://www.phontron.com/class/nn4nlp2019/)          | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL8PYTP1V4I8Ajj7sY6sdtmjgkt7eo2VMs) | 2019      |
409 | | 9.   | **Natural Language Processing with Deep Learning**  | Abigail See, Chris Manning, Richard Socher, Stanford University | [CS224n](http://web.stanford.edu/class/cs224n/)              | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLoROMvodv4rOhcuXMZkNm7j3fVwBBY42z) | 2019      |
410 | | 10.  | **Natural Language Understanding**                  | Bill MacCartney and Christopher Potts                        | [CS224U](https://web.stanford.edu/class/cs224u)              | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLoROMvodv4rObpMCir6rNNUlFAn56Js20) | S2019     |
411 | |      |                                                     |                                                              |                                                              |                                                              |           |
412 | | 11.  | **Neural Networks for Natural Language Processing** | Graham Neubig, Carnegie Mellon University                    | [CS 11-747](http://www.phontron.com/class/nn4nlp2020/schedule.html) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL8PYTP1V4I8CJ7nMxMC8aXv8WqKYwj-aJ) | S2020     |
413 | | 12.  | **Advanced Natural Language Processing**            | Mohit Iyyer, UMass Amherst                                   | [CS 685](https://people.cs.umass.edu/~miyyer/cs685)          | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLWnsVgP6CzadmQX6qevbar3_vDBioWHJL) | F2020     |
414 | | 13.  | **Machine Translation**                             | Philipp Koehn, Johns Hopkins University                      | [EN 601.468/668](http://mt-class.org/jhu/syllabus.html)      | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLQrCiUDqDLG0lQX54o9jB4phJ-SLI6ZBQ) | F2020     |
415 | | 14.  | **Neural Networks for NLP**                         | Graham Neubig, Carnegie Mellon University                    | [CS 11-747](http://www.phontron.com/class/nn4nlp2021)        | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL8PYTP1V4I8AkaHEJ7lOOrlex-pcxS-XV) | 2021      |
416 | | 15.  | **Deep Learning for Natural Language Processing**   | Kyunghyun Cho, New York University                           | [DS-GA 1011](https://drive.google.com/drive/folders/1ykXBtophaY_65VHK_8yDzZQJwfJDD5Ve) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLdH9u0f1XKW_s-c8EcgJpn_HJz5Jj1IRf) | F2021     |
417 | | 16.  | **Natural Language Processing with Deep Learning**  | Chris Manning, Stanford University                           | [CS224n](https://web.stanford.edu/class/archive/cs/cs224n/cs224n.1214/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLoROMvodv4rOSH4v6133s9LFPRHjEmbmJ) | 2021      |
418 | |      |                                                     |                                                              |                                                              |                                                              |           |
419 | 
420 | [Go to Contents :arrow_heading_up:](https://github.com/kmario23/deep-learning-drizzle#contents) 
421 | 
422 | :heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign:
423 | ###  :speaking_head: Automatic Speech Recognition :speech_balloon: :thought_balloon:
424 | :heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign:
425 | 
426 | | S.No | Course Name                              | University/Instructor(s)       | Course WebPage                                      | Lecture Videos                                               | Year      |
427 | | ---- | ---------------------------------------- | ------------------------------ | --------------------------------------------------- | ------------------------------------------------------------ | --------- |
428 | | 1.   | **Deep Learning for Speech & Language**  | UPC Barcelona                  | [DL-SL](https://telecombcn-dl.github.io/2017-dlsl/) | [Lecture-Videos](https://telecombcn-dl.github.io/2017-dlsl/) <br/> [YouTube-Videos](https://www.youtube.com/playlist?list=PL-5DCZHuHZkWeF9ljIjoC_X5gHRLNtIkU) | 2017      |
429 | | 2.   | **Speech and Audio in the Northeast**    | Many Legends, Google NYC       | [SANE-15](http://www.saneworkshop.org/sane2015/)    | [YouTube-Videos](https://www.youtube.com/playlist?list=PLBJWRPcgwk7sZOB4UTVilWWnRg84L9o5i) | 2015      |
430 | | 3.   | **Automatic Speech Recognition**         | Samudra Vijaya K, TIFR         | `None`                                              | [YouTube-Videos](https://www.youtube.com/channel/UCHk6uq1Cr9J3k5KNmIsYUNw/videos) | 2016      |
431 | | 4.   | **Speech and Audio in the Northeast**    | Many Legends, Google NYC       | [SANE-17](http://www.saneworkshop.org/sane2017/)    | [YouTube-Videos](https://www.youtube.com/playlist?list=PLBJWRPcgwk7tNLaBVu_S90ZQSblO3bwjg) | 2017      |
432 | | 5.   | **Speech and Audio in the Northeast**    | Many Legends, Google Cambridge | [SANE-18](http://www.saneworkshop.org/sane2018/)    | [YouTube-Videos](https://www.youtube.com/playlist?list=PLBJWRPcgwk7sjMANn8jqosyHIMe6DJhmn) | 2018      |
433 | |      |                                          |                                |                                                     |                                                              |           |
434 | | -1.  | **Deep Learning for Speech Recognition** | Many Legends, AoE              | `None`                                              | [YouTube-Videos](https://www.youtube.com/playlist?list=PLM4Pv4KYYzGyFYCXV6YPWAKVOR2gmHnQd) | 2015-2018 |
435 | 
436 | [Go to Contents :arrow_heading_up:](https://github.com/kmario23/deep-learning-drizzle#contents) 
437 | 
438 | 
439 | :heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign:
440 | ### :fire: Modern Computer Vision :camera_flash: :movie_camera: 
441 | :heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign:
442 | 
443 | | S.No | Course Name                                                  | University/Instructor(s)                               | Course WebPage                                               | Lecture Videos                                               | Year       |
444 | | ---- | ------------------------------------------------------------ | ------------------------------------------------------ | ------------------------------------------------------------ | ------------------------------------------------------------ | ---------- |
445 | | 1.   | **Microsoft Computer Vision Summer School** - (classical)    | Lots of Legends, Lomonosov Moscow State University     | `None`                                                       | [YouTube-Videos](https://www.youtube.com/playlist?list=PLbwKcm5vdiSYU54xFUG1zoxQTulqvIcJu) <br> [Russian-mirror](https://www.youtube.com/playlist?list=PL-_cKNuVAYAUp0eCL7KO8QY4ETY3tIDFH) | 2011       |
446 | | 2.   | **Computer Vision** - (classical)                            | Mubarak Shah, UCF                                      | [CAP-5415](http://crcv.ucf.edu/courses/CAP5415/Fall2012/index.php) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLd3hlSJsX_Imk_BPmB_H3AQjFKZS9XgZm) | 2012       |
447 | | 3.   | **Image and Multidimensional Signal Processing** - (classical) | William Hoff, Colorado School of Mines                 | [CSCI 510/EENG 510](http://inside.mines.edu/~whoff/courses/EENG510) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLyED3W677ALNv8Htn0f9Xh-AHe1aZPftv) | 2012       |
448 | | 4.   | **Computer Vision** - (classical)                            | William Hoff, Colorado School of Mines                 | [CSCI 512/EENG 512](http://inside.mines.edu/~whoff/courses/EENG512/index.htm) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL4B3F8D4A5CAD8DA3) | 2012       |
449 | | 5.   | **Image and Video Processing: From Mars to Hollywood with a Stop at the Hospital** | Guillermo Sapiro, Duke University                      | `None`                                                       | [YouTube-Videos](https://www.youtube.com/playlist?list=PLZ9qNFMHZ-A79y1StvUUqgyL-O0fZh2rs) | 2013       |
450 | | 6.   | **Multiple View Geometry** (classical)                       | Daniel Cremers, Technische Universität München         | [mvg](https://vision.in.tum.de/teaching/ss2014/mvg2014)      | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLTBdjV_4f-EJn6udZ34tht9EVIW7lbeo4) | 2013       |
451 | | 7.   | **Mathematical Methods for Robotics, Vision, and Graphics**  | Justin Solomon, Stanford University                    | [CS-205A](http://graphics.stanford.edu/courses/cs205a/)      | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLQ3UicqQtfNvQ_VzflHYKhAqZiTxOkSwi) | 2013       |
452 | | 8.   | **Computer Vision** - (classical)                            | Mubarak Shah, UCF                                      | [CAP-5415](http://crcv.ucf.edu/courses/CAP5415/Fall2014/index.php) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLd3hlSJsX_ImKP68wfKZJVIPTd8Ie5u-9) | 2014       |
453 | | 9.   | **Computer Vision for Visual Effects** (classical)           | Rich Radke, Rensselaer Polytechnic Institute           | [ECSE-6969](https://www.ecse.rpi.edu/~rjradke/cvfxcourse.html) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLuh62Q4Sv7BUJlKlt84HFqSWfW36MDd5a) | S2014      |
454 | | 10.  | **Autonomous Navigation for Flying Robots**                  | Juergen Sturm, Technische Universität München          | [Autonavx](https://jsturm.de/wp/teaching/autonavx-slides/)   | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLTBdjV_4f-EKBCUs1HmMtsnXv4JUoFrzg) | 2014       |
455 | |      |                                                              |                                                        |                                                              |                                                              |            |
456 | | 11.  | **SLAM - Mobile Robotics**                                   | Cyrill Stachniss, Universitaet Freiburg                | [RobotMapping](http://ais.informatik.uni-freiburg.de/teaching/ws13/mapping/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLgnQpQtFTOGQrZ4O5QzbIHgl3b1JHimN_) | 2014       |
457 | | 12.  | **Computational Photography**                                | Irfan Essa, David Joyner, Arpan Chakraborty            | [CP-Udacity](https://eu.udacity.com/course/computational-photography--ud955) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLAwxTw4SYaPn-unAWtRMleY4peSe4OzIY) | 2015       |
458 | | 13.  | **Introduction to Digital Image Processing**                 | Rich Radke, Rensselaer Polytechnic Institute           | [ECSE-4540](https://www.ecse.rpi.edu/~rjradke/improccourse.html) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLuh62Q4Sv7BUf60vkjePfcOQc8sHxmnDX) | S2015      |
459 | | 14.  | **Lectures on Digital Photography**                          | Marc Levoy, Stanford/Google Research                   | [LoDP](https://sites.google.com/site/marclevoylectures/)     | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL7ddpXYvFXspUN0N-gObF1GXoCA-DA-7i) | 2016       |
460 | | 15.  | **Introduction to Computer Vision** (foundation)             | Aaron Bobick, Irfan Essa, Arpan Chakraborty            | [CV-Udacity](https://eu.udacity.com/course/introduction-to-computer-vision--ud810) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLAwxTw4SYaPnbDacyrK_kB_RUkuxQBlCm) | 2016       |
461 | | 16.  | **Computer Vision**                                          | Syed Afaq Ali Shah, University of Western Australia    | `None`                                                       | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLvqB6_mDBCdlnT84LK_NvbOqcXLlOTR8j) | 2016       |
462 | | 17.  | **Photogrammetry I & II**                                    | Cyrill Stachniss, University of Bonn                   | [PG-I&II](https://www.ipb.uni-bonn.de/photogrammetry-i-ii/)  | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLgnQpQtFTOGRsi5vzy9PiQpNWHjq-bKN1) | 2016       |
463 | | 18.  | **Deep Learning for Computer Vision**                        | UPC Barcelona                                          | [DLCV-16](http://imatge-upc.github.io/telecombcn-2016-dlcv/) <br/> [DLCV-17](https://telecombcn-dl.github.io/2017-dlcv/) <br/> [DLCV-18](https://telecombcn-dl.github.io/2018-dlcv/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL-5eMc3HQTBbuaTFP4wsfD2Y2VqEfQcaP) | 2016-2018  |
464 | | 19.  | **Convolutional Neural Networks**                            | Andrew Ng, Stanford University                         | [DeepLearning.AI](https://www.deeplearning.ai/deep-learning-specialization/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLkDaE6sCZn6Gl29AoE31iwdVwSG-KnDzF) | 2017       |
465 | | 20.  | **Variational Methods for Computer Vision**                  | Daniel Cremers, Technische Universität München         | [VMCV](https://vision.in.tum.de/teaching/ws2016/vmcv2016)    | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLTBdjV_4f-EJ7A2iIH5L5ztqqrWYjP2RI) | 2017       |
466 | |      |                                                              |                                                        |                                                              |                                                              |            |
467 | | 21.  | **Winter School on Computer Vision**                         | Lots of Legends, Israel Institute for Advanced Studies | [WS-CV](http://www.as.huji.ac.il/cse)                        | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLTn74Qx5mPsSniA5tt6W-o0OGYEeKScug) | 2017       |
468 | | 22.  | **Deep Learning for Visual Computing**                       | Debdoot Sheet, IIT-Kgp                                 | [Nptel](https://onlinecourses.nptel.ac.in/noc18_ee08/preview)  [Notebooks](https://github.com/iitkliv/dlvcnptel) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLuv3GM6-gsE1Biyakccxb3FAn4wBLyfWf) | 2018       |
469 | | 23.  | **The Ancient Secrets of Computer Vision**                   | Joseph Redmon, Ali Farhadi                             | [TASCV](https://pjreddie.com/courses/computer-vision/) ; [TASCV-UW](https://courses.cs.washington.edu/courses/cse455/18sp/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLjMXczUzEYcHvw5YYSU92WrY8IwhTuq7p) | 2018       |
470 | | 24.  | **Modern Robotics**                                          | Kevin Lynch, Northwestern Robotics                     | [modern-robot](http://hades.mech.northwestern.edu/index.php/Modern_Robotics) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLggLP4f-rq02vX0OQQ5vrCxbJrzamYDfx) | 2018       |
471 | | 25.  | **Digial Image Processing**                                  | Alex Bronstein, Technion                               | [CS236860](https://vistalab-technion.github.io/cs236860/info/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLM0a6Z788YAZOxUyWda9y3N_i2upIj1Ep) | 2018       |
472 | | 26.  | **Mathematics of Imaging** - Variational Methods and Optimization in Imaging | Lots of Legends, Institut Henri Poincaré               | [Workshop-1](http://www.ihp.fr/sites/default/files/conf1-04_au_08_fevr-imaging2019.pdf) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL9kd4mpdvWcAzD5Aq-P1TrLLiYckrloxw) | 2019       |
473 | | 27.  | **Deep Learning for Video**                                  | Xavier Giró, UPC Barcelona                             | [deepvideo](https://mcv-m6-video.github.io/deepvideo-2019/)  | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL-5eMc3HQTBbPY-627Gornj09pZrNQgPD) | 2019       |
474 | | 28.  | **Statistical modeling for shapes and imaging**              | Lots of Legends, Institut Henri Poincaré, Paris        | [workshop-2](https://imaging-in-paris.github.io/semester2019/workshop2prog) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL9kd4mpdvWcAzD5Aq-P1TrLLiYckrloxw) | 2019       |
475 | | 29.  | **Imaging and machine learning**                             | Lots of Legends, Institut Henri Poincaré, Paris        | [workshop-3](https://imaging-in-paris.github.io/semester2019/workshop3prog) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL9kd4mpdvWcAzD5Aq-P1TrLLiYckrloxw) | 2019       |
476 | | 30.  | **Computer Vision**                                          | Jayanta Mukhopadhyay, IIT Kgp                          | [CV-nptel](https://nptel.ac.in/courses/106/105/106105216/)   | [YouTube-Lectures](https://nptel.ac.in/courses/106/105/106105216/) | 2019       |
477 | |      |                                                              |                                                        |                                                              |                                                              |            |
478 | | 31.  | **Deep Learning for Computer Vision**                        | Justin Johnson, UMichigan                              | [EECS 498-007](https://web.eecs.umich.edu/~justincj/teaching/eecs498/) | [Lecture-Videos](http://leccap.engin.umich.edu/leccap/site/jhygcph151x25gjj1f0) <br/> [YouTube-Lectures](https://www.youtube.com/playlist?list=PL5-TkQAfAZFbzxjBHtzdVCWE0Zbhomg7r) | 2019       |
479 | | 32.  | **Sensors and State Estimation 2**                           | Cyrill Stachniss, University of Bonn                   | `None`                                                       | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLgnQpQtFTOGQh_J16IMwDlji18SWQ2PZ6) | S2020      |
480 | | 33.  | **Computer Vision III: Detection, Segmentation and Tracking** | Laura Leal-Taixé, TU München                           | [CV3DST](https://dvl.in.tum.de/teaching/cv3dst-ss20/)        | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLog3nOPCjKBneGyffEktlXXMfv1OtKmCs) | S2020      |
481 | | 34.  | **Advanced Deep Learning for Computer Vision**               | Laura Leal-Taixé and Matthias Nießner, TU München      | [ADL4CV](https://dvl.in.tum.de/teaching/adl4cv-ss20)         | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLog3nOPCjKBnjhuHMIXu4ISE4Z4f2jm39) | S2020      |
482 | | 35.  | **Computer Vision: Foundations**                             | Fred Hamprecht, Universität Heidelberg                 | [CVF](https://hci.iwr.uni-heidelberg.de/ial/cvf)             | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLuRaSnb3n4kRAbnmiyGd77hyoGzO9wPde) | SS2020     |
483 | | 36.  | **MIT Vision Seminar**                                       | Lots of Legends, MIT                                   | [MIT-Vision](https://sites.google.com/view/visionseminar/past-talks) | [YouTube-Lectures](https://www.youtube.com/channel/UCLMiFkFyfcNnZs6iwYLPI9g/videos) | 2015-now   |
484 | | 37.  | **TUM AI Guest Lectures**                                    | Lots of Legends, Technische Universität München        | [TUM-AI](https://niessner.github.io/TUM-AI-Lecture-Series)   | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLQ8Y4kIIbzy8kMlz7cRqz-BjbdyWsfLXt) | 2020 - now |
485 | | 38.  | **Seminar on 3D Geometry & Vision**                          | Lots of Legends, Virtual                               | [3DGV seminar](https://3dgv.github.io)                       | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLZk0jtN0g8e-xVTfsiV67q8Iz1cZO_FpV) | 2020 - now |
486 | | 39.  | **Event-based Robot Vision**                                 | Guillermo Gallego, Technische Universität Berlin       | [EVIS-SS20](https://sites.google.com/view/guillermogallego/teaching/event-based-robot-vision) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL03Gm3nZjVgUFYUh3v5x8jVonjrGfcal8) | 2020 - now |
487 | | 40.  | **Deep Learning for Computer Vision**                        | Vineeth Balasubramanian, IIT Hyderabad                 | [DL-CV'20](https://onlinecourses.nptel.ac.in/noc20_cs88/preview) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLyqSpQzTE6M_PI-rIz4O1jEgffhJU9GgG) | 2020       |
488 | |      |                                                              |                                                        |                                                              |                                                              |            |
489 | | 41.  | **Deep Learning for Visual Computing**                       | Peter Wonka, KAUST, SA                                 | `NOne`                                                       | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLMpQLEui13s2DHbw6kTTxwQma8rehlfZE) | 2020       |
490 | | 42.  | **Computer Vision**                                          | Yogesh Rawat, University of Central Florida            | [CAP5415-CV](https://www.crcv.ucf.edu/courses/cap5415-fall-2020/schedule/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLd3hlSJsX_Ikm5il1HgmDB_z62BeoikFX) | F2020      |
491 | | 43.  | **Multimedia Signal Processing**                             | Mark Hasegawa-Johnson, UIUC                            | [ECE-417 MSP](https://courses.engr.illinois.edu/ece417/fa2020/) | [Lecture Videos](https://mediaspace.illinois.edu/channel/ECE%20417/26816181) | F2020      |
492 | | 44.  | **Computer Vision**                                          | Andreas Geiger, Universität Tübingen                   | [Comp.Vis](https://uni-tuebingen.de/fakultaeten/mathematisch-naturwissenschaftliche-fakultaet/fachbereiche/informatik/lehrstuehle/autonomous-vision/lectures/computer-vision/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL05umP7R6ij35L2MHGzis8AEHz7mg381_) | S2021      |
493 | | 45.  | **3D Computer Vision**                                       | Lee Gim Hee, National Univeristy of Singapura          | `None`                                                       | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLxg0CGqViygP47ERvqHw_v7FVnUovJeaz) | 2021       |
494 | | 46.  | **Deep Learning for Computer Vision: Fundamentals and Applications** | T. Dekel et al., Weizmann Institute of Science         | [DL4CV](https://dl4cv.github.io/schedule.html)               | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL_Z2_U9MIJdNgFM7-f2fZ9ZxjVRP_jhJv) | S2021      |
495 | | 47.  | **Current Topics in ML Methods in 3D and Geometric Deep Learning** | Animesh Garg  & others, University of Toronto          | [CSC 2547](http://www.pair.toronto.edu/csc2547-w21)          | [YouTube-Lectures](https://www.youtube.com/channel/UCrsmAXnwu6sgccWevW12Dfg/videos) | 2021       |
496 | | 48.  | **First Principles of Computer Vision**                      | Shree K. Nayar, Columbia University                    | [FPCV](https://fpcv.cs.columbia.edu)                         | [YouTube-Lectures](https://www.youtube.com/channel/UCf0WB91t8Ky6AuYcQV0CcLw/videos) | 2021       |
497 | | 49.  | **Self-Driving Cars**                                        | Andreas Geiger, Universität Tübingen                   | [SDC'21](https://uni-tuebingen.de/de/123611)                 | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL05umP7R6ij321zzKXK6XCQXAaaYjQbzr) | W2021      |
498 | |      |                                                              |                                                        |                                                              |                                                              |            |
499 | 
500 | [Go to Contents :arrow_heading_up:](https://github.com/kmario23/deep-learning-drizzle#contents) 
501 | 
502 | :heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign:
503 | ### :star2: Boot Camps or Summer Schools :maple_leaf:
504 | :heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign:
505 | 
506 | | S.No | Course Name                                             | University/Instructor(s)                                 | Course WebPage                                               | Lecture Videos                                               | Year      |
507 | | ---- | ------------------------------------------------------- | -------------------------------------------------------- | ------------------------------------------------------------ | ------------------------------------------------------------ | --------- |
508 | | 1.   | **Deep Learning, Feature Learning**                     | Lots of Legends, IPAM UCLA                               | [GSS-2012](https://www.ipam.ucla.edu/programs/summer-schools/graduate-summer-school-deep-learning-feature-learning/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLHyI3Fbmv0SdzMHAy0aN59oYnLy5vyyTA) | 2012      |
509 | | 2.   | **Big Data Boot Camp**                                  | Lots of Legends, Simons Institute                    | [Big Data](https://simons.berkeley.edu/workshops/schedule/316) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLgKuh-lKre13RmUC2AybRvVAxO5DEMIBH) | 2013      |
510 | | 3. | **Machine Learning Summer School** | Lots of Legends, MPI-IS Tübingen | [MLSS-13](http://mlss.tuebingen.mpg.de/2013/2013/index.html) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLqJm7Rc5-EXFv6RXaPZzzlzo93Hl0v91E) | 2013 |
511 | | 4 | **Graduate Summer School: Computer Vision** | Lots of Legends, IPAM-UCLA | [GSS-CV](http://www.ipam.ucla.edu/programs/summer-schools/graduate-summer-school-computer-vision/) | [Video-Lectures](http://www.ipam.ucla.edu/programs/summer-schools/graduate-summer-school-computer-vision/?tab=schedule) | 2013 |
512 | | 5. | **Machine Learning Summer School** | Lots of Legends, Reykjavik University | [MLSS-14](http://mlss2014.hiit.fi/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLqdbxUnkqOw2nKn7VxYqIrKWcqRkQYOsF) | 2014 |
513 | | 6. | **Machine Learning Summer School** | Lots of Legends, Pittsburgh | [MLSS-14](http://www.mlss2014.com) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLZSO_6-bSqHQCIYxE3ycGLXHMjK3XV7Iz) | 2014 |
514 | | 7. | **Deep Learning Summer School** | Lots of Legends, Université de Montréal | [DLSS-15](https://sites.google.com/site/deeplearningsummerschool/home) | [YouTube-Lectures](http://videolectures.net/deeplearning2015_montreal/) | 2015 |
515 | | 8. | **Biomedical Image Analysis Summer School** | Lots of Legends, CentraleSupelec, Paris | `None` | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLgSHH6boFf5uJAUT4ZRiAZc_ofXolkAGK) | 2015 |
516 | | 9. | **Mathematics of Signal Processing**                    | Lots of Legends, Hausdorff Institute for Mathematics | [SigProc](http://www.him.uni-bonn.de/signal-processing-2016/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLul8LCT3AJqSQo3lr5RbwxJ92RsgRuDtx) | 2016      |
517 | | 10. | **Microsoft Research - Machine Learning Course**        | S V N Vishwanathan and Prateek Jain MS-Research          | `None`                                                       | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL34iyE0uXtxo7vPXGFkmm6KbgZQwjf9Kf) | 2016      |
518 | |  |  |  |  |  |  |
519 | | 11. | **Deep Learning Summer School**                         | Lots of Legends, Université de Montréal                  | [DL-SS-16](https://sites.google.com/site/deeplearningsummerschool2016/home) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL5bqIc6XopCbb-FvnHmD1neVlQKwGzQyR) | 2016      |
520 | | 12. | **Lisbon Machine Learning School** | Lots of Legends, Instituto Superior Técnico, Portugal | [LxMLS-16](http://lxmls.it.pt/2016/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLToLj8M4ao-fymxXBIOU6sF1NGFLb5EiX) | 2016 |
521 | | 13. | **Machine Learning Advances and Applications Seminar**  | Lots of Legends, Fields Institute, University of Toronto | [MLAAS-16](http://www.fields.utoronto.ca/activities/16-17/machine-learning) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLfsVAYSMwskuQcRkuDApP40lX_i08d0QK) <br/> [Video-Lectures](http://www.fields.utoronto.ca/video-archive/event/2267) | 2016-2017 |
522 | | 14. | **Machine Learning Advances and Applications Seminar**  | Lots of Legends, Fields Institute, University of Toronto | [MLAAS-17](http://www.fields.utoronto.ca/activities/17-18/machine-learning) | [Video Lectures](http://www.fields.utoronto.ca/video-archive/event/2487) | 2017-2018 |
523 | | 15. | **Machine Learning Summer School** | Lots of Legends, MPI-IS Tübingen | [MLSS-17](http://mlss.tuebingen.mpg.de/2017/index.html) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLqJm7Rc5-EXFUOvoYCdKikfck8YeUCnl9) | 2017 |
524 | | 16. | **Representation Learning**                             | Lots of Legends, Simons Institute                    | [RepLearn](https://simons.berkeley.edu/workshops/abstracts/3750) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLgKuh-lKre13UNV4ztsWUXciUZ7x_ZDHz) | 2017      |
525 | | 17. | **Foundations of Machine Learning**                     | Lots of Legends, Simons Institute                  | [ML-BootCamp](https://simons.berkeley.edu/workshops/abstracts/3748) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLgKuh-lKre11GbZWneln-VZDLHyejO7YD) | 2017      |
526 | | 18. | **Optimization, Statistics, and Uncertainty**           | Lots of Legends, Simons Institute                    | [Optim-Stats](https://simons.berkeley.edu/workshops/abstracts/4795) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLgKuh-lKre13ACD44z2FH-IVP1e8ip5JO) | 2017      |
527 | | 19. | **Deep Learning: Theory, Algorithms, and Applications** | Lots of Legends, TU-Berlin                         | [DL: TAA](http://doc.ml.tu-berlin.de/dlworkshop2017/)        | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLJOzdkh8T5kqCNV_v1w2tapvtJDZYiohW) | 2017      |
528 | | 20. | **Deep Learning and Reinforcement Learning Summer School** | Lots of Legends, Université de Montréal                                   | [DLRL-2017](https://mila.quebec/en/cours/deep-learning-summer-school-2017/)   | [Lecture-videos](http://videolectures.net/deeplearning2017_montreal/)          | 2017 |
529 | |  |  |  |  |  |  |
530 | | 21. | **Statistical Physics Methods in Machine Learning** | Lots of Legends, International Centre for Theoretical Sciences, TIFR | [SPMML](https://www.icts.res.in/discussion-meeting/SPMML2017) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL04QVxpjcnjhtL3IIVyFRMOgdhWtPn7YJ) | 2017 |
531 | | 22. | **Lisbon Machine Learning School** | Lots of Legends, Instituto Superior Técnico, Portugal | [LxMLS-17](http://lxmls.it.pt/2017/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLToLj8M4ao-fuRfnzEJCCnvuW2_FeJ73N) | 2017 |
532 | | 23. | **Interactive Learning** | Lots of Legends, Simons Institute, Berkeley | [IL-2017](https://simons.berkeley.edu/workshops/schedule/3749) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLgKuh-lKre10T2POF-WzXh0ckdpyvANUx) | 2017 |
533 | | 24. | **Computational Challenges in Machine Learning** | Lots of Legends, Simons Institute, Berkeley | [CCML-17](https://simons.berkeley.edu/workshops/schedule/3751) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLgKuh-lKre12eXz4dnvc8oervo2_Af4iU) | 2017 |
534 | | 25. | **Foundations of Data Science**                         | Lots of Legends, Simons Institute                   | [DS-BootCamp](https://simons.berkeley.edu/workshops/abstracts/6680) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLgKuh-lKre13r1Qrnrejj3f498-NurSf3) | 2018      |
535 | | 26. | **Deep Learning and Bayesian Methods**           | Lots of Legends, HSE Moscow                          | [DLBM-SS](http://deepbayes.ru/2018/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLe5rNUydzV9Q01vWCP9BV7NhJG3j7mz62) | 2018      |
536 | | 27. | **New Deep Learning Techniques**                        | Lots of Legends, IPAM UCLA                           | [IPAM-Workshop](https://www.ipam.ucla.edu/programs/workshops/new-deep-learning-techniques/?tab=schedule) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLHyI3Fbmv0SdM0zXj31HWjG9t9Q0v2xYN) | 2018      |
537 | | 28. | **Deep Learning and Reinforcement Learning Summer School** | Lots of Legends, University of Toronto | [DLRL-2018](https://dlrlsummerschool.ca/2018-event/) | [Lecture-videos](http://videolectures.net/DLRLsummerschool2018_toronto/) | 2018 |
538 | | 29. | **Machine Learning Summer School** | Lots of Legends, Universidad Autónoma de Madrid, Spain | [MLSS-18](http://mlss.ii.uam.es/mlss2018/index.html) | [YouTube-Lectures](https://www.youtube.com/channel/UCbPJHr__eIor_7jFH3HmVHQ/videos) <br/> [Course-videos](http://mlss.ii.uam.es/mlss2018/speakers.html) | 2018 |
539 | | 30. | **Theoretical Basis of Machine Learning** | Lots of Legends, International Centre for Theoretical Sciences, TIFR | [TBML-18](https://www.icts.res.in/discussion-meeting/tbml2018) | [Lecture-Videos](https://www.icts.res.in/discussion-meeting/tbml2018/talks) <br/> [YouTube-Videos](https://www.youtube.com/playlist?list=PL04QVxpjcnjj1DgnXxFBo2fkSju4r-ggr) | 2018 |
540 | |  |  |  |  |  |  |
541 | | 31. | **Polish View on Machine Learning** | Lots of Legends, Warsaw | [PLinML-18](https://plinml.mimuw.edu.pl/) | [YouTube-Videos](https://www.youtube.com/playlist?list=PLoaWrlj9TDhPcA6N9dZQ6GPXboYuumDRp) | 2018 |
542 | | 32. | **Big Data Analysis in Astronomy** | Lots of Legends, Tenerife | [BDAA-18](http://research.iac.es/winterschool/2018/pages/book-ws2018.php) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLM4Pv4KYYzGx42W5pSp3Itetp0u-PENtI) | 2018 |
543 | | 33. | **Machine Learning Advances and Applications Seminar**  | Lots of Legends, Fields Institute, University of Toronto | [MLASS](http://www.fields.utoronto.ca/activities/18-19/machine-learning) | [Video Lectures](http://www.fields.utoronto.ca/video-archive/event/2681) | 2018-2019 |
544 | | 34. | **MIFODS- ML, Stats, ToC seminar**                      | Lots of Legends, MIT                                     | [MIFODS-seminar](http://mifods.mit.edu/seminar.php)          | [Lecture-videos](http://mifods.mit.edu/seminar.php)          | 2018-2019 |
545 | | 35. | **Learning Machines Seminar Series** | Lots of Legends, Cornell Tech | [LMSS](https://lmss.tech.cornell.edu/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLycW2Yy79JuxbQZ9uHEu_NS3cGNomhL2A) | 2018-now |
546 | | 36. | **Machine Learning Summer School** | Lots of Legends, South Africa | [MLSS'19](https://mlssafrica.com/programme-schedule/) | [YouTube-Lectures](https://www.youtube.com/channel/UC722CmQVgcLtxt_jXr3RyWg/videos) | 2019 |
547 | | 37. | **Deep Learning Boot Camp** | Lots of Legends, Simons Institute, Berkeley | [DLBC-19](https://simons.berkeley.edu/workshops/schedule/10624) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLgKuh-lKre12c2Il9mNX0Cmp9Z4oFNrQh) | 2019 |
548 | | 38. | **Frontiers of Deep Learning** | Lots of Legends, Simons Institute, Berkeley | [FoDL-19](https://simons.berkeley.edu/workshops/schedule/10627) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLgKuh-lKre11ekU7g-Z_qsvjDD8cT-hi9) | 2019 |
549 | | 39. | **Mathematics of data: Structured representations for sensing, approximation and learning** | Lots of Legends, The Alan Turing Institute, London | [MoD-19](https://www.turing.ac.uk/sites/default/files/2019-05/agenda_9_3.pdf) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLuD_SqLtxSdX_w1Ztexpzl_EJgFQSkWez) | 2019 |
550 | | 40. | **Deep Learning and Bayesian Methods** | Lots of Legends, HSE Moscow | [DLBM-SS](http://deepbayes.ru/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLe5rNUydzV9QHe8VDStpU0o8Yp63OecdW) | 2019 |
551 | |  |  |  |  |  |  |
552 | | 41. | **The Mathematics of Deep Learning and Data Science** | Lots of Legends, Isaac Newton Institute, Cambridge | [MoDL-DS](https://gateway.newton.ac.uk/event/ofbw46) | [Lecture-Videos](https://gateway.newton.ac.uk/event/ofbw46/programme) | 2019 |
553 | | 42. | **Geometry of Deep Learning** | Lots of Legends, MSR Redmond | [GoDL](https://www.microsoft.com/en-us/research/event/ai-institute-2019) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLD7HFcN7LXRe30qq36It2XCljxc340O_d) | 2019 |
554 | | 43. | **Deep Learning for Science School** | Many folks, LBNL, Berkeley | [DLfSS](https://dl4sci-school.lbl.gov/agenda) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL20S5EeApOSvfvEyhCPOUzU7zkBcR5-eL) | 2019 |
555 | | 44. | **Emerging Challenges in Deep Learning** | Lots of Legends, Simons Institute, Berkeley | [ECDL](https://simons.berkeley.edu/workshops/schedule/10629) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLgKuh-lKre10BpafDrv0fg2VNUweWXWVd) | 2019 |
556 | | 45. | **Full Stack Deep Learning** | Pieter Abbeel and many others, UC Berkeley | [FSDL-M19](https://fullstackdeeplearning.com/march2019) | [YouTube-Lectures-Day-1](https://www.youtube.com/playlist?list=PL1T8fO7ArWlcf3Hc4VMEVBlH8HZm_NbeB) <br/> [Day-2](https://www.youtube.com/playlist?list=PL1T8fO7ArWlf6TWwdstb-PcwlubnlrKrm) | 2019 |
557 | | 46. | **Algorithmic and Theoretical aspects of Machine Learning** | Lots of legends, IIIT-Bengaluru | [ACM-ML](https://india.acm.org/education/machine-learning) <br/> [nptel](https://nptel.ac.in/courses/128/106/128106011/) | [YouTube-Lectures](https://nptel.ac.in/courses/128/106/128106011) | 2019 |
558 | | 47. | **Deep Learning and Reinforcement Learning Summer School** | Lots of Legends, AMII, Edmonton, Canada | [DLRL-2019](https://dlrlsummerschool.ca/past-years) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLKlhhkvvU8-aXmPQZNYG_e-2nTd0tJE8v) | 2019 |
559 | | 48. | **Mathematics of Machine Learning** - Summer Graduate School | Lots of Legends, University of Washington | [MoML-SGS](http://www.msri.org/summer_schools/866#schedule), [MoML-SS](http://mathofml.cs.washington.edu/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLTPQEx-31JXhguCush5J7OGnEORofoCW9) | 2019 |
560 | | 49. | **Workshop on Theory of Deep Learning: Where next?** | Lots of Legends, IAS, Princeton University | [WTDL](https://www.math.ias.edu/wtdl) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLdDZb3TwJPZ5dqqg_S-rgJqSFeH4DQqFQ) | 2019 |
561 | | 50. | **Computational Vision Summer School** | Lots of Legends, Black Forest, Germany | [CVSS-2019](http://orga.cvss.cc/program-cvss-2019/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLeCNfJWZKqxsvidOlVLtWq9s7sIsX1QTC) | 2019 |
562 | | | | | | | |
563 | | 51. | **Learning under complex structure** | Lots of Legends, MIT | [LUCS](https://mifods.mit.edu/complex.php) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLM4Pv4KYYzGwhIHcaY6zYR7M9hhFO4Vud) | 2020 |
564 | | 52. | **Machine Learning Summer School** | Lots of Legends, MPI-IS Tübingen (virtual) | [MLSS](http://mlss.tuebingen.mpg.de/2020/schedule.html) | [YouTube-Lectures](https://www.youtube.com/channel/UCBOgpkDhQuYeVVjuzS5Wtxw/videos) | SS2020 |
565 | | 53. | **Eastern European Machine Learning Summer School** | Lots of Legends, Kraków, Poland (virtual) | [EEML](https://www.eeml.eu/program) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLaKY4p4V3gE1j01FOY2FeglV4jRntQj84) | S2020 |
566 | | 54. | **Lisbon Machine Learning Summer School** | Lots of Legends, Lisbon, Portugal (virtual) | [LxMLS](http://lxmls.it.pt/2020/?page_id=19) | [YouTube-Lectures](https://www.youtube.com/channel/UCkVFZWgT1jR75UvSLGP9_mw) | S2020 |
567 | | 55. | **Workshop on New Directions in Optimization, Statistics and Machine Learning** | Lots of Legends,  Institute of Advanced Study, Princeton | [ML-Opt new dir.](https://www.ias.edu/video/workshop/2020/0415-16) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLdDZb3TwJPZ4Ri6i0MIdesIEpYK4lx17Q) | 2020 |
568 | | 56. | **Mediterranean Machine Learning School** | Lots of Legends, Italy (virtual) | [M2L-school](https://www.m2lschool.org/talks) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLF-wkqRv4u1YRbfnwN8cXXyrmXld-sked) | 2021 |
569 | | 57. | **Mathematics of Machine Learning - One World Seminar** | Lots of Legends, Virtual | [1W-ML](https://sites.google.com/view/oneworldml/past-events) | [YouTube-Lectures](https://www.youtube.com/channel/UCz7WlgXs20CzugkfxhFCNFg/videos) | 2020 - now |
570 | | 58. | **Deep Learning Theory Summer School** | Lots of Legends, Princeton University (virtual) | [DLT'21](https://deep-learning-summer-school.princeton.edu) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PL2mB9GGlueJj_FNjJ8RWgz4Nc_hCSXfMU) | 2021 |
571 | | | | | | | |
572 | 
573 | [Go to Contents :arrow_heading_up:](https://github.com/kmario23/deep-learning-drizzle#contents)
574 | 
575 | :heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign:
576 | ### :bird: Bird's Eye view of A(G)I :eagle:
577 | :heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign:
578 | 
579 | | S.No | Course Name                            | University/Instructor(s)                                 | Course WebPage                                               | Lecture Videos                                               | Year      |
580 | | ---- | -------------------------------------- | -------------------------------------------------------- | ------------------------------------------------------------ | ------------------------------------------------------------ | --------- |
581 | | 1.   | **Artificial General Intelligence**    | Lots of Legends, MIT                                     | [6.S099-AGI](https://agi.mit.edu/)                           | [Lecture-Videos](https://agi.mit.edu/)                       | 2018-2019 |
582 | | 2.   | **AI Podcast**                         | Lots of Legends, MIT                                     | [AI-Pod](https://lexfridman.com/ai/)                         | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLrAXtmErZgOdP_8GztsuKi9nrraNbKKp4) | 2018-2019 |
583 | | 3.   | **NYU - AI Seminars**                  | Lots of Legends, NYU                                     | [modern-AI](https://engineering.nyu.edu/academics/departments/electrical-and-computer-engineering/ece-seminar-series/modern-artificial) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLhwo5ntex8iY9xhpSwWas451NgVuqBE7U) | 2017-now  |
584 | | 4.   | **Deep Learning: Alchemy or Science?** | Lots of Legends, Institute for Advanced Study, Princeton | [DLAS](https://video.ias.edu/deeplearning/2019/0222) <br/> [Agenda](https://www.math.ias.edu/tml/dlasagenda) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLdDZb3TwJPZ7aAxhIHALBoh8l6-UxmMNP) | 2019      |
585 | |      |                                        |                                                          |                                                              |                                                              |           |
586 | 
587 | [Go to Contents :arrow_heading_up:](https://github.com/kmario23/deep-learning-drizzle#contents)
588 | 
589 | :heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign:
590 | ### To-Do :running:
591 | :heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign:
592 | 
593 | :white_large_square: Optimization courses which form the foundation for ML, DL, RL
594 | 
595 | :white_large_square: Computer Vision courses which are DL & ML heavy
596 | 
597 | :white_large_square: Speech recognition courses which are DL heavy
598 | 
599 | :white_large_square: Structured Courses on Geometric, Graph Neural Networks
600 | 
601 | :white_large_square: Section on Autonomous Vehicles
602 | 
603 | :white_large_square: Section on Computer Graphics with ML/DL focus
604 | 
605 | :heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign:
606 | 
607 | [Go to Contents :arrow_heading_up:](https://github.com/kmario23/deep-learning-drizzle#contents) 
608 | 
609 | :heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign:
610 | 
611 | 
612 | ### Around the Web :earth_asia:
613 | 
614 |  - [Montreal.AI](http://www.montreal.ai/ai4all.pdf)
615 |  - [UPC-DLAI-2018](https://telecombcn-dl.github.io/2018-dlai/)
616 |  - [UPC-DLAI-2019](https://telecombcn-dl.github.io/dlai-2019/)
617 |  - [www.hashtagtechgeek.com](https://www.hashtagtechgeek.com/2019/10/250-machine-learning-deep-learning-videos-courseware.html)
618 |  - [UPC-Barcelona, IDL-2020](https://telecombcn-dl.github.io/idl-2020/) 
619 |  - [UPC-DLAI-2020](https://telecombcn-dl.github.io/dlai-2020) 
620 | 
621 | :heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign:
622 | 
623 | 
624 | ### Contributions :pray:
625 | 
626 | If you find a course that fits in any of the above categories (i.e. DL, ML, RL, CV, NLP), **and** the course has lecture videos (with slides being optional), then please raise an issue or send a PR by updating the course according to the above format.
627 | 
628 | 
629 | :heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign:
630 | 
631 | 
632 | ### Support :moneybag:
633 | 
634 | **Optional:** If you're a kind Samaritan and want to support me, please do so if possible, for which I would eternally be thankful and, most importantly, your contribution imbues me with greater motivation to work, particularly in hard times :pray:
635 | 
636 | [![](https://www.paypalobjects.com/en_US/i/btn/btn_donateCC_LG.gif)](https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&hosted_button_id=NT3EATS5N35WU)
637 | 
638 | 
639 | Vielen lieben Dank! :blue_heart: 
640 | 
641 | :heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign:
642 | 
643 | ###  :gift_heart: :mortar_board: :mortar_board: :mortar_board: :mortar_board: :mortar_board: :mortar_board: :mortar_board::mortar_board: :mortar_board: :mortar_board: :mortar_board: :mortar_board: :mortar_board: :mortar_board: :mortar_board: :mortar_board: :mortar_board: :mortar_board: :mortar_board: :mortar_board: :mortar_board::mortar_board: :mortar_board: :mortar_board: :mortar_board: :mortar_board: :mortar_board: :mortar_board: :gift_heart: 
644 | 
645 | :heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign::heavy_minus_sign:
646 | 
647 | 


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/markdown2html_py/fontawesome-animations.txt:
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1 | BEG: <i class='fab fa-jedi-order faa-pulse animated' style='font-size:40px;color:saddlebrown'></i> &nbsp;
2 | END: &nbsp; <i class='fas fa-cloud-rain faa-passing animated' style='font-size:33px;color:dodgerblue'></i> <i class='fas fa-cloud-showers-heavy faa-passing animated' style='font-size:33px;color:dodgerblue'></i> <i class='fas fa-cloud-showers-heavy faa-passing animated' style='font-size:33px;color:dodgerblue'></i>
3 | 


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/markdown2html_py/html_foot.txt:
--------------------------------------------------------------------------------
 1 | 
 2 | <a href="https://github.com/kmario23/deep-learning-drizzle" class="github">
 3 |    <img style="position: absolute; top: 0; right: 0; border: 0;" src="https://s3.amazonaws.com/github/ribbons/forkme_right_darkblue_121621.png" alt="Fork me on GitHub"  class="github"/>
 4 | </a>
 5 | 
 6 | </body>
 7 | <hr style="background-color:#E02461">
 8 | 
 9 | <p class="text_center">
10 |    <!-- to get the counts, watch, and forks from github repository, using http://ghbtns.com/ -->
11 |     <iframe src="https://ghbtns.com/github-btn.html?user=kmario23&repo=deep-learning-drizzle&type=star&count=true" frameborder="0" scrolling="0" width="110px" height="17px"></iframe>
12 |     Made with <i class='fas fa-heart faa-pulse faa-fast animated' style='font-size:23px;color:crimson'></i> and maintained by 
13 |     <a href="https://github.com/kmario23" style="text-decoration:none"> @kmario23</a>, <b>S</b>aarland <b>I</b>nformatics <b>C</b>ampus &nbsp;
14 |     <iframe src="https://ghbtns.com/github-btn.html?user=kmario23&repo=deep-learning-drizzle&type=fork&count=true" frameborder="0" scrolling="0" width="110px" height="17px"></iframe> 
15 | <iframe src="https://ghbtns.com/github-btn.html?user=kmario23&repo=deep-learning-drizzle&type=watch&count=true&v=2" frameborder="0" scrolling="0" width="110px" height="17px"></iframe>
16 | </p>
17 | 
18 | </html>
19 | 


--------------------------------------------------------------------------------
/markdown2html_py/html_head.txt:
--------------------------------------------------------------------------------
  1 | <!doctype html>
  2 | 
  3 | <html lang="en">
  4 | 
  5 | <head>
  6 |   <meta charset="utf-8">
  7 |   <meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no">
  8 |   <link href="https://afeld.github.io/emoji-css/emoji.css" rel="stylesheet">
  9 |   <link href="https://emoji.tryhtml.org/emoji.css" rel="stylesheet">
 10 | 
 11 |   <script src='https://kit.fontawesome.com/a076d05399.js'></script>
 12 |   <!-- fontawesome animation (local copy)-->
 13 |   <link href="./font-awesome/css/font-awesome-animation.min.css" rel="stylesheet">
 14 | 
 15 |   <title>Deep Learning Drizzle</title>
 16 |   <style>
 17 |     h1{
 18 |        text-align: center;
 19 |     }
 20 | 
 21 |     body{
 22 |          background-color: #E5E0DF;
 23 |     }
 24 |     .heart{
 25 |            color:#e25555;
 26 |           }
 27 |     .text_center {text-align: center;}
 28 | 
 29 |     hr{
 30 |        height: 2px;
 31 |        color: #DBA345;
 32 |        background-color: #DBA345;
 33 |        border: none;
 34 |      }
 35 | 
 36 |    /* DIV ids for all tables' title */
 37 |    #contents {color: red;}
 38 | 
 39 |    .centerTable { margin: 0px auto; }
 40 | 
 41 |    /* table of contents table*/
 42 |    #toc {
 43 |          font-family: "Trebuchet MS", Arial, Helvetica, sans-serif;
 44 |          border-collapse: collapse;
 45 |          width: 50%;  /* adjust table width */
 46 |    }
 47 |    #toc td, #toc th {
 48 |                      border: 1px solid black; 
 49 |                      padding: 5px 10px 5px 20px;  /* top, right, bottom, left */
 50 |                      text-align: left;
 51 |    }
 52 |    #toc tr:nth-child(even){background-color: #f5f5f5;}
 53 |    #toc tr:hover {background-color: #539a9a;}
 54 |    #toc th {
 55 |             padding-top: 12px;
 56 |             padding-bottom: 12px;
 57 |             text-align: center;
 58 |             background-color: #539a9a;
 59 |             color: white;
 60 |    }
 61 | 
 62 | 
 63 |    /* deep learning or deep neural networks table */
 64 |    #dldnn {
 65 |          font-family: "Trebuchet MS", Arial, Helvetica, sans-serif;
 66 |          border-collapse: collapse;
 67 |          width: 100%;  /* adjust table width */
 68 |    }
 69 |    #dldnn td, #dldnn th {
 70 |                      border: 1px solid black; 
 71 |                      padding: 5px 10px 5px 20px;  /* top, right, bottom, left */
 72 |                      text-align: left;
 73 |    }
 74 |    #dldnn tr:nth-child(even){background-color: #f5f5f5;}
 75 |    #dldnn tr:hover {background-color: #539a9a;}
 76 |    #dldnn th {
 77 |             padding-top: 12px;
 78 |             padding-bottom: 12px;
 79 |             text-align: center;
 80 |             background-color: #539a9a;
 81 |             color: white;
 82 |    }
 83 | 
 84 | 
 85 |   /* Machine Learning fundamentals table */
 86 |    #mlfund {
 87 |          font-family: "Trebuchet MS", Arial, Helvetica, sans-serif;
 88 |          border-collapse: collapse;
 89 |          width: 100%;  /* adjust table width */
 90 |    }
 91 |    #mlfund td, #mlfund th {
 92 |                      border: 1px solid black; 
 93 |                      padding: 5px 10px 5px 20px;  /* top, right, bottom, left */
 94 |                      text-align: left;
 95 |    }
 96 |    #mlfund tr:nth-child(even){background-color: #f5f5f5;}
 97 |    #mlfund tr:hover {background-color: #539a9a;}
 98 |    #mlfund th {
 99 |             padding-top: 12px;
100 |             padding-bottom: 12px;
101 |             text-align: center;
102 |             background-color: #539a9a;
103 |             color: white;
104 |    }
105 | 
106 |   /* Optimization for Machine Learning table */
107 |    #opt4ml {
108 |          font-family: "Trebuchet MS", Arial, Helvetica, sans-serif;
109 |          border-collapse: collapse;
110 |          width: 100%;  /* adjust table width */
111 |    }
112 |    #opt4ml td, #opt4ml th {
113 |                      border: 1px solid black; 
114 |                      padding: 5px 10px 5px 20px;  /* top, right, bottom, left */
115 |                      text-align: left;
116 |    }
117 |    #opt4ml tr:nth-child(even){background-color: #f5f5f5;}
118 |    #opt4ml tr:hover {background-color: #539a9a;}
119 |    #opt4ml th {
120 |             padding-top: 12px;
121 |             padding-bottom: 12px;
122 |             text-align: center;
123 |             background-color: #539a9a;
124 |             color: white;
125 |    }
126 | 
127 | 
128 |   /* General Machine Learning table */
129 |    #genml {
130 |          font-family: "Trebuchet MS", Arial, Helvetica, sans-serif;
131 |          border-collapse: collapse;
132 |          width: 100%;  /* adjust table width */
133 |    }
134 |    #genml td, #genml th {
135 |                      border: 1px solid black; 
136 |                      padding: 5px 10px 5px 20px;  /* top, right, bottom, left */
137 |                      text-align: left;
138 |    }
139 |    #genml tr:nth-child(even){background-color: #f5f5f5;}
140 |    #genml tr:hover {background-color: #539a9a;}
141 |    #genml th {
142 |             padding-top: 12px;
143 |             padding-bottom: 12px;
144 |             text-align: center;
145 |             background-color: #539a9a;
146 |             color: white;
147 |    }
148 | 
149 | 
150 |   /* Reinforcement Learning table */
151 |    #reinf {
152 |          font-family: "Trebuchet MS", Arial, Helvetica, sans-serif;
153 |          border-collapse: collapse;
154 |          width: 100%;  /* adjust table width */
155 |    }
156 |    #reinf td, #reinf th {
157 |                      border: 1px solid black; 
158 |                      padding: 5px 10px 5px 20px;  /* top, right, bottom, left */
159 |                      text-align: left;
160 |    }
161 |    #reinf tr:nth-child(even){background-color: #f5f5f5;}
162 |    #reinf tr:hover {background-color: #539a9a;}
163 |    #reinf th {
164 |             padding-top: 12px;
165 |             padding-bottom: 12px;
166 |             text-align: center;
167 |             background-color: #539a9a;
168 |             color: white;
169 |    }
170 | 
171 | 
172 |   /* Bayesian Deep Learning table */
173 |    #bayesdl {
174 |          font-family: "Trebuchet MS", Arial, Helvetica, sans-serif;
175 |          border-collapse: collapse;
176 |          width: 100%;  /* adjust table width */
177 |    }
178 |    #bayesdl td, #bayesdl th {
179 |                      border: 1px solid black; 
180 |                      padding: 5px 10px 5px 20px;  /* top, right, bottom, left */
181 |                      text-align: left;
182 |    }
183 |    #bayesdl tr:nth-child(even){background-color: #f5f5f5;}
184 |    #bayesdl tr:hover {background-color: #539a9a;}
185 |    #bayesdl th {
186 |             padding-top: 12px;
187 |             padding-bottom: 12px;
188 |             text-align: center;
189 |             background-color: #539a9a;
190 |             color: white;
191 |    }
192 | 
193 | 
194 |   /* Medical Imaging table */
195 |    #medimg {
196 |          font-family: "Trebuchet MS", Arial, Helvetica, sans-serif;
197 |          border-collapse: collapse;
198 |          width: 100%;  /* adjust table width */
199 |    }
200 |    #medimg td, #medimg th {
201 |                      border: 1px solid black; 
202 |                      padding: 5px 10px 5px 20px;  /* top, right, bottom, left */
203 |                      text-align: left;
204 |    }
205 |    #medimg tr:nth-child(even){background-color: #f5f5f5;}
206 |    #medimg tr:hover {background-color: #539a9a;}
207 |    #medimg th {
208 |             padding-top: 12px;
209 |             padding-bottom: 12px;
210 |             text-align: center;
211 |             background-color: #539a9a;
212 |             color: white;
213 |    }
214 | 
215 | 
216 | 
217 |   /* Probabilistic Graphical Models */
218 |    #probgm {
219 |          font-family: "Trebuchet MS", Arial, Helvetica, sans-serif;
220 |          border-collapse: collapse;
221 |          width: 100%;  /* adjust table width */
222 |    }
223 |    #probgm td, #probgm th {
224 |                      border: 1px solid black; 
225 |                      padding: 5px 10px 5px 20px;  /* top, right, bottom, left */
226 |                      text-align: left;
227 |    }
228 |    #probgm tr:nth-child(even){background-color: #f5f5f5;}
229 |    #probgm tr:hover {background-color: #539a9a;}
230 |    #probgm th {
231 |             padding-top: 12px;
232 |             padding-bottom: 12px;
233 |             text-align: center;
234 |             background-color: #539a9a;
235 |             color: white;
236 |    }
237 | 
238 |   /* Graph Neural Networks */
239 |    #graphnn {
240 |          font-family: "Trebuchet MS", Arial, Helvetica, sans-serif;
241 |          border-collapse: collapse;
242 |          width: 100%;  /* adjust table width */
243 |    }
244 |    #graphnn td, #graphnn th {
245 |                      border: 1px solid black; 
246 |                      padding: 5px 10px 5px 20px;  /* top, right, bottom, left */
247 |                      text-align: left;
248 |    }
249 |    #graphnn tr:nth-child(even){background-color: #f5f5f5;}
250 |    #graphnn tr:hover {background-color: #539a9a;}
251 |    #graphnn th {
252 |             padding-top: 12px;
253 |             padding-bottom: 12px;
254 |             text-align: center;
255 |             background-color: #539a9a;
256 |             color: white;
257 |    }
258 | 
259 |   /* Natural Language Processing */
260 |   #nlpnn {
261 |          font-family: "Trebuchet MS", Arial, Helvetica, sans-serif;
262 |          border-collapse: collapse;
263 |          width: 100%;  /* adjust table width */
264 |    }
265 |    #nlpnn td, #nlpnn th {
266 |                      border: 1px solid black; 
267 |                      padding: 5px 10px 5px 20px;  /* top, right, bottom, left */
268 |                      text-align: left;
269 |    }
270 |    #nlpnn tr:nth-child(even){background-color: #f5f5f5;}
271 |    #nlpnn tr:hover {background-color: #539a9a;}
272 |    #nlpnn th {
273 |             padding-top: 12px;
274 |             padding-bottom: 12px;
275 |             text-align: center;
276 |             background-color: #539a9a;
277 |             color: white;
278 |    }
279 | 
280 |   /* Automatic Speech Recognition */
281 |   #asrnn {
282 |          font-family: "Trebuchet MS", Arial, Helvetica, sans-serif;
283 |          border-collapse: collapse;
284 |          width: 100%;  /* adjust table width */
285 |    }
286 |    #asrnn td, #asrnn th {
287 |                      border: 1px solid black; 
288 |                      padding: 5px 10px 5px 20px;  /* top, right, bottom, left */
289 |                      text-align: left;
290 |    }
291 |    #asrnn tr:nth-child(even){background-color: #f5f5f5;}
292 |    #asrnn tr:hover {background-color: #539a9a;}
293 |    #asrnn th {
294 |             padding-top: 12px;
295 |             padding-bottom: 12px;
296 |             text-align: center;
297 |             background-color: #539a9a;
298 |             color: white;
299 |    }
300 | 
301 |   /* Modern Computer Vision */
302 |   #cvnn {
303 |          font-family: "Trebuchet MS", Arial, Helvetica, sans-serif;
304 |          border-collapse: collapse;
305 |          width: 100%;  /* adjust table width */
306 |    }
307 |    #cvnn td, #cvnn th {
308 |                      border: 1px solid black; 
309 |                      padding: 5px 10px 5px 20px;  /* top, right, bottom, left */
310 |                      text-align: left;
311 |    }
312 |    #cvnn tr:nth-child(even){background-color: #f5f5f5;}
313 |    #cvnn tr:hover {background-color: #539a9a;}
314 |    #cvnn th {
315 |             padding-top: 12px;
316 |             padding-bottom: 12px;
317 |             text-align: center;
318 |             background-color: #539a9a;
319 |             color: white;
320 |    }
321 | 
322 |   /* Boot Camps or Summer Schools */
323 |   #bcss {
324 |          font-family: "Trebuchet MS", Arial, Helvetica, sans-serif;
325 |          border-collapse: collapse;
326 |          width: 100%;  /* adjust table width */
327 |    }
328 |    #bcss td, #bcss th {
329 |                      border: 1px solid black; 
330 |                      padding: 5px 10px 5px 20px;  /* top, right, bottom, left */
331 |                      text-align: left;
332 |    }
333 |    #bcss tr:nth-child(even){background-color: #f5f5f5;}
334 |    #bcss tr:hover {background-color: #539a9a;}
335 |    #bcss th {
336 |             padding-top: 12px;
337 |             padding-bottom: 12px;
338 |             text-align: center;
339 |             background-color: #539a9a;
340 |             color: white;
341 |    }
342 | 
343 |   /* Bird's Eye-view of A(G)I */
344 |   #aginn {
345 |          font-family: "Trebuchet MS", Arial, Helvetica, sans-serif;
346 |          border-collapse: collapse;
347 |          width: 100%;  /* adjust table width */
348 |    }
349 |    #aginn td, #aginn th {
350 |                      border: 1px solid black; 
351 |                      padding: 5px 10px 5px 20px;  /* top, right, bottom, left */
352 |                      text-align: left;
353 |    }
354 |    #aginn tr:nth-child(even){background-color: #f5f5f5;}
355 |    #aginn tr:hover {background-color: #539a9a;}
356 |    #aginn th {
357 |             padding-top: 12px;
358 |             padding-bottom: 12px;
359 |             text-align: center;
360 |             background-color: #539a9a;
361 |             color: white;
362 |    }
363 | 
364 | </style>
365 | 
366 | </head>
367 | 
368 | <body>
369 | 
370 | 


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/markdown2html_py/markdown2html.py:
--------------------------------------------------------------------------------
  1 | from pyemojify import emojify
  2 | import re
  3 | import markdown
  4 | from lxml import etree
  5 | 
  6 | 
  7 | # read contents from github README.md
  8 | with open('README.md', 'r') as f:
  9 |     markdn = f.readlines()
 10 | 
 11 | # read some header contents for html
 12 | with open('html_head.txt', 'r') as f:
 13 |     html_head = f.readlines()
 14 | 
 15 | # read some footer contents for html
 16 | with open('html_foot.txt', 'r') as f:
 17 |     html_foot = f.readlines()
 18 | 
 19 | # read some fontawesome stuff for html
 20 | with open('fontawesome-animations.txt', 'r') as f:
 21 |     fontawesome_animations = []
 22 |     for line in f:
 23 |         line = line.strip().replace('BEG:', '')
 24 |         line = line.strip().replace('END:', '')
 25 |         fontawesome_animations.append(line)
 26 | 
 27 | # to color code the emojies
 28 | em1 = '<i class="em em-'
 29 | em2 = '"></i>'
 30 | 
 31 | # link to specific part of page
 32 | ahref1 = '<a href="'
 33 | ahrefm = '" style="text-decoration:none">'
 34 | ahref2 = '</a>'
 35 | 
 36 | host_url = "https://deep-learning-drizzle.github.io/"
 37 | host_page = "index.html"
 38 | 
 39 | # heading size 1
 40 | h1b = "<h1> "
 41 | h1e = " </h1>"
 42 | 
 43 | # heading size 2
 44 | h2b = "<h2>"
 45 | h2e = " </h2>"
 46 | 
 47 | # heading size 5
 48 | h5b = "<h5>"
 49 | h5e = "</h5>"
 50 | 
 51 | # line break html
 52 | lb_html = '<br/>'
 53 | 
 54 | # html url construction
 55 | url1 = '<a href="'
 56 | url2 = ' "style="text-decoration:none">'
 57 | url3 = '</a>'
 58 | 
 59 | # center align something
 60 | center1 = '<p style="text-align:center">'
 61 | center2 = '</p>'
 62 | 
 63 | # navigation to toc
 64 | g2c = "Go to Contents "
 65 | divcont = '#contents'
 66 | 
 67 | # navigation to top of table
 68 | divdnns = '#dldnn'
 69 | divmlfund = '#mlfund'
 70 | divopt4ml = '#opt4ml'
 71 | divgenml = '#genml'
 72 | divreinf = '#reinf'
 73 | divbdl = '#bayesdl'
 74 | divmi = '#medimg'
 75 | divpgm = '#probgm'
 76 | divgnn = '#graphnn'
 77 | divnlp = '#nlpnn'
 78 | divasr = '#asrnn'
 79 | divcvnn = '#cvnn'
 80 | divbcss = '#bcss'
 81 | divagi = '#aginn'
 82 | 
 83 | 
 84 | def prettify_emoji(word):
 85 |     return em1 + word + em2
 86 | 
 87 | 
 88 | # down arrow
 89 | down_arrow = ":arrow_heading_down:"
 90 | down_arrow_prettified = prettify_emoji("arrow_heading_down")
 91 | 
 92 | # up arrow
 93 | up_arrow = ":arrow_heading_up:"
 94 | up_arrow_prettified = prettify_emoji("arrow_heading_up")
 95 | 
 96 | 
 97 | def parse_markdown_url(text):
 98 |     """
 99 |     A simple helper function to convert markdwon url to a tuple:
100 |     [url_text](https://) => ('url_text', 'https://')
101 |     From: https://stackoverflow.com/a/23395483
102 |     """
103 |     # Anything that isn't a square closing bracket
104 |     name_regex = "[^]]+"
105 |     # http:// or https:// followed by anything but a closing paren
106 |     url_regex = "http[s]?://[^)]+"
107 | 
108 |     markup_regex = '\[({0})]\(\s*({1})\s*\)'.format(name_regex, url_regex)
109 | 
110 |     extracted = []
111 |     for match in re.findall(markup_regex, text):
112 |         extracted.append(match)
113 |     return extracted
114 | 
115 | # s = '| 1.   | **Neural Networks for Machine Learning**              | Geoffrey Hinton, University of Toronto         | [Lecture-Slides](http://www.cs.toronto.edu/~hinton/coursera_slides.html) <br/> [CSC321-tijmen](https://www.cs.toronto.edu/~tijmen/csc321/) | [YouTube-Lectures](https://www.youtube.com/playlist?list=PLoRl3Ht4JOcdU872GhiYWf6jwrk_SNhz9) <br/> [UofT-mirror](https://www.cs.toronto.edu/~hinton/coursera_lectures.html) | 2012 <br/> 2014 |'
116 | # print(parse_markdown_url(s))
117 | 
118 | 
119 | def convert_markdown_url2html(text, extracted):
120 |     if len(extracted) == 1:
121 |         url_text, url_link = extracted[0][0], extracted[0][1]
122 |         text = text.replace(url_text, "")
123 |         text = text.replace(url_link, "")
124 |         url = url1 + url_link + url2 + url_text + url3
125 |         text = text.replace("[]()", url)
126 | 
127 |         # center align
128 |         text = center1 + text + center2
129 |         return text
130 | 
131 | 
132 | def table_topic_emoji_processor(line):
133 |     if ":" in line:
134 |         matches = re.findall(r":(.*?):", line)
135 |         # print(matches)
136 |         for emj in matches:
137 |             m = ":" + emj + ":"
138 |             if m in line:
139 |                 # line = line.replace(m, prettify_emoji(emj)) # uncomment this line to enable emojis
140 |                 line = line.replace(m, '')
141 |     return line.strip()
142 | 
143 | 
144 | def add_navigation_button():
145 |     """
146 |     adds a go to contents button
147 |     """
148 |     pret = prettify_emoji("arrow_heading_up")
149 |     pret = url1 + host_url + host_page + divcont + url2 + h5b + g2c + pret + h5e + url3
150 |     return pret
151 | 
152 | 
153 | def replace_github_url_with_webpage_url(line, matches):
154 |     for lnk in matches:
155 |         lnk = lnk.replace('(', '')
156 |         lnk = lnk.replace(')', '')
157 |         if "deep-learning-deep-neural-networks" in line:
158 |             line = line.replace(lnk, host_url + host_page + divdnns)
159 |         if "probabilistic-graphical-models" in line:
160 |             line = line.replace(lnk, host_url + host_page + divpgm)
161 |         if "bayesian-deep-learning" in line:
162 |             line = line.replace(lnk, host_url + host_page + divbdl)
163 |         if "medical-imaging" in line:
164 |             line = line.replace(lnk, host_url + host_page + divmi)
165 |         if "machine-learning-fundamentals" in line:
166 |             line = line.replace(lnk, host_url + host_page + divmlfund)
167 |         if "natural-language-processing" in line:
168 |             line = line.replace(lnk, host_url + host_page + divnlp)
169 |         if "optimization-for-machine-learning" in line:
170 |             line = line.replace(lnk, host_url + host_page + divopt4ml)
171 |         if "automatic-speech-recognition-speech" in line:
172 |             line = line.replace(lnk, host_url + host_page + divasr)
173 |         if "general-machine-learning" in line:
174 |             line = line.replace(lnk, host_url + host_page + divgenml)
175 |         if "modern-computer-vision" in line:
176 |             line = line.replace(lnk, host_url + host_page + divcvnn)
177 |         if "reinforcement-learning" in line:
178 |             line = line.replace(lnk, host_url + host_page + divreinf)
179 |         if "boot-camps-or-summer-schools" in line:
180 |             line = line.replace(lnk, host_url + host_page + divbcss)
181 |         if "graph-neural-networks" in line:
182 |             line = line.replace(lnk, host_url + host_page + divgnn)
183 |         if "birds-eye-view-of-agi" in line:
184 |             line = line.replace(lnk, host_url + host_page + divagi)
185 | 
186 |     return line
187 | 
188 | 
189 | # desired html
190 | with open('index.html', 'w') as f:
191 |     # write header info
192 |     f.write(''.join(html_head))
193 | 
194 |     heavy_minus_tracker = 0
195 |     all_lines = []
196 |     table_of_contents = []
197 |     dl_dnn = []
198 |     ml_fund = []
199 |     opt_ml = []
200 |     gen_ml = []
201 |     reinf_learn = []
202 |     bayes_dl = []
203 |     med_img = []
204 |     prob_gm = []
205 |     graph_nn = []
206 |     nlp_nn = []
207 |     asr_nn = []
208 |     cv_nn = []
209 |     bcss = []
210 |     agi_nn = []
211 |     for line in markdn:
212 |         if line.startswith('# '):
213 |             line = line.replace('# ', '')
214 |             # very first line
215 |             if ":" in line:
216 |                 temp = []
217 |                 line_contents = line.split(" ")
218 |                 for w in line_contents:
219 |                     if ":" in w:
220 |                         continue  # comment this line to enable emoji
221 |                         w = w.replace(":", "")
222 |                         w = prettify_emoji(w)
223 |                         temp.append(w)
224 |                     else:
225 |                         temp.append(w)
226 |                 line = " ".join(temp)
227 |                 del temp
228 |                 line = h1b + fontawesome_animations[0] + line + fontawesome_animations[1] + h1e
229 |                 all_lines.append(line)
230 |                 # line separator
231 |                 all_lines.append('<hr>')
232 | 
233 |         # quote
234 |         if ":books:" in line:
235 |             # extract URL/text
236 |             extracted = parse_markdown_url(line)
237 |             line = convert_markdown_url2html(line, extracted)
238 | 
239 |             # line = line.replace(lb_html, '')  # remove line break
240 |             line = line.replace(":books:", prettify_emoji("books"))  # color code emoji
241 |             line = line.replace('**"', '<strong>"')  # bold text
242 |             line = line.replace('"**', '"</strong>')
243 |             all_lines.append(line)
244 | 
245 |         # if heavy minus, just pass
246 |         if ":heavy_minus_sign:" in line:
247 |             heavy_minus_tracker += 1
248 |             continue
249 | 
250 |         # table of contents
251 |         if "### Contents" in line:
252 |             line = line.replace('### ', '')
253 |             line = '<div id="contents"> ' + ahref1 + host_page + divcont + ahrefm + h2b + line + h2e + ahref2
254 |             line = line + ' </div>'
255 |             # print(line)
256 |             all_lines.append(line)
257 | 
258 |         # group toc in a list and then process them
259 |         if "| " in line and heavy_minus_tracker == 2:
260 |             line = line.replace(down_arrow, down_arrow_prettified)
261 |             extr = parse_markdown_url(line)
262 |             matches = re.findall(r"\(https.*?\)", line)
263 |             line = replace_github_url_with_webpage_url(line, matches)
264 |             table_of_contents.append(line)
265 | 
266 |         # signifies end of table; now convert them to html table
267 |         if heavy_minus_tracker == 3:
268 |             # convert toc markdown to html table
269 |             toc_html = markdown.markdown("".join(table_of_contents), extensions=['markdown.extensions.tables'])
270 |             toc_html = toc_html.replace('<table>', '<table id="toc" class="centerTable">')  # center align table
271 |             all_lines.append(toc_html)
272 |             all_lines.append('<br/> <br/>')
273 |             all_lines.append('<hr>')
274 | 
275 |         # Depp Neurl Networks TABLE
276 |         # table Deep Learning (Deep Neural Networks)
277 |         if "## :tada: Deep Learning" in line:
278 |             line = line.replace('## ', '')
279 |             line = table_topic_emoji_processor(line)
280 |             line = ahref1 + host_page + divdnns + ahrefm + h2b + line + h2e + ahref2
281 |             # print(line)
282 |             all_lines.append(line)
283 | 
284 |         # group DNN table in a list and then process them
285 |         if "| " in line and heavy_minus_tracker == 4:
286 |             dl_dnn.append(line)
287 | 
288 |         # signifies end of table; now convert them to html table
289 |         if heavy_minus_tracker == 5:
290 |             # convert DL DNN markdown to html table
291 |             dl_dnn_html = markdown.markdown("".join(dl_dnn), extensions=['markdown.extensions.tables'])
292 |             dl_dnn_html = dl_dnn_html.replace('<table>', '<table id="dldnn">')  # center align table
293 |             # remove underline in url links
294 |             dl_dnn_html = dl_dnn_html.replace('">', '" style="text-decoration:none">')
295 |             # print(dl_dnn_html)
296 |             all_lines.append(dl_dnn_html)
297 | 
298 |             # navigation to top
299 |             all_lines.append(add_navigation_button())
300 |             all_lines.append("<hr>")
301 | 
302 |         # ML FUNDAMENTALS TABLE
303 |         # ML fundamentals
304 |         if "### :cupid: Machine Learning" in line:
305 |             line = line.replace('### ', '')
306 |             line = table_topic_emoji_processor(line)
307 |             line = ahref1 + divmlfund + ahrefm + h2b + line + h2e + ahref2
308 |             # print(line)
309 |             all_lines.append(line)
310 | 
311 |          # group ML fundamentals table in a list and then process them
312 |         if "| " in line and heavy_minus_tracker == 6:
313 |             ml_fund.append(line)
314 | 
315 |         # signifies end of table; now convert them to html table
316 |         if heavy_minus_tracker == 7:
317 |             # convert ML Funda markdown to html table
318 |             ml_fund_html = markdown.markdown("".join(ml_fund), extensions=['markdown.extensions.tables'])
319 |             ml_fund_html = ml_fund_html.replace('<table>', '<table id="mlfund">')  # center align table
320 |             # remove underline in url links
321 |             ml_fund_html = ml_fund_html.replace('">', '" style="text-decoration:none">')
322 |             # print(ml_fund_html)
323 |             all_lines.append(ml_fund_html)
324 | 
325 |             # navigation to top
326 |             all_lines.append(add_navigation_button())
327 |             all_lines.append("<hr>")
328 | 
329 |         # OPT for ML TABLE
330 |         # Optimization for Machine Learning
331 |         if "### :cupid: Optimization for Machine Learning" in line:
332 |             line = line.replace('### ', '')
333 |             line = table_topic_emoji_processor(line)
334 |             line = ahref1 + divopt4ml + ahrefm + h2b + line + h2e + ahref2
335 |             # print(line)
336 |             all_lines.append(line)
337 | 
338 |          # group OPT 4 ML table in a list and then process them
339 |         if "| " in line and heavy_minus_tracker == 8:
340 |             opt_ml.append(line)
341 | 
342 |         # signifies end of table; now convert them to html table
343 |         if heavy_minus_tracker == 9:
344 |             # convert Optim 4 ML markdown to html table
345 |             opt_ml_html = markdown.markdown("".join(opt_ml), extensions=['markdown.extensions.tables'])
346 |             opt_ml_html = opt_ml_html.replace('<table>', '<table id="opt4ml">')  # center align table
347 |             # remove underline in url links
348 |             opt_ml_html = opt_ml_html.replace('">', '" style="text-decoration:none">')
349 |             # print(opt_ml_html)
350 |             all_lines.append(opt_ml_html)
351 | 
352 |             # navigation to top
353 |             all_lines.append(add_navigation_button())
354 |             all_lines.append("<hr>")
355 | 
356 |         # GENERAL ML TABLE
357 |         # General Machine Learning
358 |         if "### :cupid: General Machine" in line:
359 |             line = line.replace('### ', '')
360 |             line = table_topic_emoji_processor(line)
361 |             line = ahref1 + divgenml + ahrefm + h2b + line + h2e + ahref2
362 |             # print(line)
363 |             all_lines.append(line)
364 | 
365 |         # group General ML table in a list and then process them
366 |         if "| " in line and heavy_minus_tracker == 10:
367 |             gen_ml.append(line)
368 | 
369 |         # signifies end of table; now convert them to html table
370 |         if heavy_minus_tracker == 11:
371 |             # convert General ML markdown to html table
372 |             gen_ml_html = markdown.markdown("".join(gen_ml), extensions=['markdown.extensions.tables'])
373 |             gen_ml_html = gen_ml_html.replace('<table>', '<table id="genml">')  # center align table
374 |             # remove underline in url links
375 |             gen_ml_html = gen_ml_html.replace('">', '" style="text-decoration:none">')
376 |             # print(gen_ml_html)
377 |             all_lines.append(gen_ml_html)
378 | 
379 |             # navigation to top
380 |             all_lines.append(add_navigation_button())
381 |             all_lines.append("<hr>")
382 | 
383 |         # REINFORCEMENT LEARNING TABLE
384 |         # Reinforcement Learning
385 |         if "### :balloon: Reinforcement" in line:
386 |             line = line.replace('### ', '')
387 |             line = table_topic_emoji_processor(line)
388 |             line = ahref1 + divreinf + ahrefm + h2b + line + h2e + ahref2
389 |             # print(line)
390 |             all_lines.append(line)
391 | 
392 |         # group Reinforcement Learning table in a list and then process them
393 |         if "| " in line and heavy_minus_tracker == 12:
394 |             reinf_learn.append(line)
395 | 
396 |         # signifies end of table; now convert them to html table
397 |         if heavy_minus_tracker == 13:
398 |             # convert reinforce learn markdown to html table
399 |             reinf_learn_html = markdown.markdown("".join(reinf_learn), extensions=['markdown.extensions.tables'])
400 |             reinf_learn_html = reinf_learn_html.replace('<table>', '<table id="reinf">')  # center align table
401 |             # remove underline in url links
402 |             reinf_learn_html = reinf_learn_html.replace('">', '" style="text-decoration:none">')
403 |             # print(reinf_learn_html)
404 |             all_lines.append(reinf_learn_html)
405 | 
406 |             # navigation to top
407 |             all_lines.append(add_navigation_button())
408 |             all_lines.append("<hr>")
409 | 
410 |         # PROBABILISTIC GRAPHICAL MODELS TABLE
411 |         # Probabilistic Graphical Models
412 |         if "### :loudspeaker: Probabilistic Graphical Models" in line:
413 |             line = line.replace('### ', '')
414 |             line = table_topic_emoji_processor(line)
415 |             line = ahref1 + divpgm + ahrefm + h2b + line + h2e + ahref2
416 |             # print(line)
417 |             all_lines.append(line)
418 | 
419 |         # group Probabilistic Graphical Models table in a list and then process them
420 |         if "| " in line and heavy_minus_tracker == 14:
421 |             prob_gm.append(line)
422 | 
423 |         # signifies end of table; now convert them to html table
424 |         if heavy_minus_tracker == 15:
425 |             # convert Probabilistic Graphical Models markdown to html table
426 |             prob_gm_html = markdown.markdown("".join(prob_gm), extensions=['markdown.extensions.tables'])
427 |             prob_gm_html = prob_gm_html.replace('<table>', '<table id="probgm">')  # center align table
428 |             # remove underline in url links
429 |             prob_gm_html = prob_gm_html.replace('">', '" style="text-decoration:none">')
430 |             # print(prob_gm_html)
431 |             all_lines.append(prob_gm_html)
432 | 
433 |             # navigation to top
434 |             all_lines.append(add_navigation_button())
435 |             all_lines.append("<hr>")
436 | 
437 |         # BAYESIAN DEEP LEARNING TABLE
438 |         # Bayesian Deep Learning
439 |         if "## :game_die: Bayesian" in line:
440 |             line = line.replace('## ', '')
441 |             line = table_topic_emoji_processor(line)
442 |             line = ahref1 + divbdl + ahrefm + h2b + line + h2e + ahref2
443 |             # print(line)
444 |             all_lines.append(line)
445 | 
446 |         # group Bayesian Deep Learning table in a list and then process them
447 |         if "| " in line and heavy_minus_tracker == 16:
448 |             bayes_dl.append(line)
449 | 
450 |         # signifies end of table; now convert them to html table
451 |         if heavy_minus_tracker == 17:
452 |             # convert bayes deep learn markdown to html table
453 |             bayes_dl_html = markdown.markdown("".join(bayes_dl), extensions=['markdown.extensions.tables'])
454 |             bayes_dl_html = bayes_dl_html.replace('<table>', '<table id="bayesdl">')  # center align table
455 |             # remove underline in url links
456 |             reinf_learn_html = bayes_dl_html.replace('">', '" style="text-decoration:none">')
457 |             # print(bayes_dl_html)
458 |             all_lines.append(bayes_dl_html)
459 | 
460 |             # navigation to top
461 |             all_lines.append(add_navigation_button())
462 |             all_lines.append("<hr>")
463 | 
464 |         # MEDICAL IMAGING TABLE
465 |         # Medical Imaging
466 |         if "## :movie_camera: Medical" in line:
467 |             line = line.replace('## ', '')
468 |             line = table_topic_emoji_processor(line)
469 |             line = ahref1 + divmi + ahrefm + h2b + line + h2e + ahref2
470 |             # print(line)
471 |             all_lines.append(line)
472 | 
473 |         # group Medical Imaging table in a list and then process them
474 |         if "| " in line and heavy_minus_tracker == 18:
475 |             med_img.append(line)
476 | 
477 |         # signifies end of table; now convert them to html table
478 |         if heavy_minus_tracker == 19:
479 |             # convert medical imaging markdown to html table
480 |             med_img_html = markdown.markdown("".join(med_img), extensions=['markdown.extensions.tables'])
481 |             med_img_html = med_img_html.replace('<table>', '<table id="medimg">')  # center align table
482 |             # remove underline in url links
483 |             med_img_html = med_img_html.replace('">', '" style="text-decoration:none">')
484 |             # print(med_img_html)
485 |             all_lines.append(med_img_html)
486 | 
487 |             # navigation to top
488 |             all_lines.append(add_navigation_button())
489 |             all_lines.append("<hr>")
490 | 
491 |         # GRAPH NEURAL NETWORKS TABLE
492 |         # Graph Neural Networks
493 |         if "## :tada: Graph Neural Networks" in line:
494 |             line = line.replace('## ', '')
495 |             line = table_topic_emoji_processor(line)
496 |             line = ahref1 + divgnn + ahrefm + h2b + line + h2e + ahref2
497 |             # print(line)
498 |             all_lines.append(line)
499 | 
500 |         # group Graph Neural Networks table in a list and then process them
501 |         if "| " in line and heavy_minus_tracker == 20:
502 |             graph_nn.append(line)
503 | 
504 |         # signifies end of table; now convert them to html table
505 |         if heavy_minus_tracker == 21:
506 |             # convert Graph Neural Networks markdown to html table
507 |             graph_nn_html = markdown.markdown("".join(graph_nn), extensions=['markdown.extensions.tables'])
508 |             graph_nn_html = graph_nn_html.replace('<table>', '<table id="graphnn">')  # center align table
509 |             # remove underline in url links
510 |             graph_nn_html = graph_nn_html.replace('">', '" style="text-decoration:none">')
511 |             # print(graph_nn_html)
512 |             all_lines.append(graph_nn_html)
513 | 
514 |             # navigation to top
515 |             all_lines.append(add_navigation_button())
516 |             all_lines.append("<hr>")
517 | 
518 |         # NATURAL LANGUAGE PROCESSING TABLE
519 |         # Natural Language Processing
520 |         if "### :hibiscus: Natural Language Processing" in line:
521 |             line = line.replace('### ', '')
522 |             line = table_topic_emoji_processor(line)
523 |             line = ahref1 + divnlp + ahrefm + h2b + line + h2e + ahref2
524 |             # print(line)
525 |             all_lines.append(line)
526 | 
527 |         # group Natural Language Processing table in a list and then process them
528 |         if "| " in line and heavy_minus_tracker == 22:
529 |             nlp_nn.append(line)
530 | 
531 |         # signifies end of table; now convert them to html table
532 |         if heavy_minus_tracker == 23:
533 |             # convert Graph Neural Networks markdown to html table
534 |             nlp_nn_html = markdown.markdown("".join(nlp_nn), extensions=['markdown.extensions.tables'])
535 |             nlp_nn_html = nlp_nn_html.replace('<table>', '<table id="nlpnn">')  # center align table
536 |             # remove underline in url links
537 |             nlp_nn_html = nlp_nn_html.replace('">', '" style="text-decoration:none">')
538 |             # print(nlp_nn_html)
539 |             all_lines.append(nlp_nn_html)
540 | 
541 |             # navigation to top
542 |             all_lines.append(add_navigation_button())
543 |             all_lines.append("<hr>")
544 | 
545 |         # Automatic Speech Recognition TABLE
546 |         # Automatic Speech Recognition
547 |         if "###  :speaking_head: Automatic Speech Recognition" in line:
548 |             line = line.replace('### ', '')
549 |             line = table_topic_emoji_processor(line)
550 |             line = ahref1 + divasr + ahrefm + h2b + line + h2e + ahref2
551 |             # print(line)
552 |             all_lines.append(line)
553 | 
554 |         # group Automatic Speech Recognition table in a list and then process them
555 |         if "| " in line and heavy_minus_tracker == 24:
556 |             asr_nn.append(line)
557 | 
558 |         # signifies end of table; now convert them to html table
559 |         if heavy_minus_tracker == 25:
560 |             # convert Automatic Speech Recognition markdown to html table
561 |             asr_nn_html = markdown.markdown("".join(asr_nn), extensions=['markdown.extensions.tables'])
562 |             asr_nn_html = asr_nn_html.replace('<table>', '<table id="asrnn">')  # center align table
563 |             # remove underline in url links
564 |             asr_nn_html = asr_nn_html.replace('">', '" style="text-decoration:none">')
565 |             # print(asr_nn_html)
566 |             all_lines.append(asr_nn_html)
567 | 
568 |             # navigation to top
569 |             all_lines.append(add_navigation_button())
570 |             all_lines.append("<hr>")
571 | 
572 |         # Modern Computer Vision TABLE
573 |         # Modern Computer Vision
574 |         if "### :fire: Modern Computer Vision" in line:
575 |             line = line.replace('### ', '')
576 |             line = table_topic_emoji_processor(line)
577 |             line = ahref1 + divcvnn + ahrefm + h2b + line + h2e + ahref2
578 |             # print(line)
579 |             all_lines.append(line)
580 | 
581 |         # group Modern Computer Vision table in a list and then process them
582 |         if "| " in line and heavy_minus_tracker == 26:
583 |             cv_nn.append(line)
584 | 
585 |         # signifies end of table; now convert them to html table
586 |         if heavy_minus_tracker == 27:
587 |             # convert Modern Computer Vision markdown to html table
588 |             cv_nn_html = markdown.markdown("".join(cv_nn), extensions=['markdown.extensions.tables'])
589 |             cv_nn_html = cv_nn_html.replace('<table>', '<table id="cvnn">')  # center align table
590 |             # remove underline in url links
591 |             cv_nn_html = cv_nn_html.replace('">', '" style="text-decoration:none">')
592 |             # print(cv_nn_html)
593 |             all_lines.append(cv_nn_html)
594 | 
595 |             # navigation to top
596 |             all_lines.append(add_navigation_button())
597 |             all_lines.append("<hr>")
598 | 
599 |         # Boot Camps or Summer Schools TABLE
600 |         # Boot Camps or Summer Schools
601 |         if "### :star2: Boot Camps or Summer Schools" in line:
602 |             line = line.replace('### ', '')
603 |             line = table_topic_emoji_processor(line)
604 |             line = ahref1 + divbcss + ahrefm + h2b + line + h2e + ahref2
605 |             # print(line)
606 |             all_lines.append(line)
607 | 
608 |         # group Boot Camps or Summer Schools table in a list and then process them
609 |         if "| " in line and heavy_minus_tracker == 28:
610 |             bcss.append(line)
611 | 
612 |         # signifies end of table; now convert them to html table
613 |         if heavy_minus_tracker == 29:
614 |             # convert Boot Camps or Summer Schools markdown to html table
615 |             bcss_html = markdown.markdown("".join(bcss), extensions=['markdown.extensions.tables'])
616 |             bcss_html = bcss_html.replace('<table>', '<table id="bcss">')  # center align table
617 |             # remove underline in url links
618 |             bcss_html = bcss_html.replace('">', '" style="text-decoration:none">')
619 |             # print(bcss_html)
620 |             all_lines.append(bcss_html)
621 | 
622 |             # navigation to top
623 |             all_lines.append(add_navigation_button())
624 |             all_lines.append("<hr>")
625 | 
626 |         # Bird's Eye view of A(G)I TABLE
627 |         # Bird's Eye view of A(G)I
628 |         if "### :bird: Bird" in line:
629 |             line = line.replace('### ', '')
630 |             line = table_topic_emoji_processor(line)
631 |             line = ahref1 + divagi + ahrefm + h2b + line + h2e + ahref2
632 |             # print(line)
633 |             all_lines.append(line)
634 | 
635 |         # group Bird's Eye view of A(G)I table in a list and then process them
636 |         if "| " in line and heavy_minus_tracker == 30:
637 |             agi_nn.append(line)
638 | 
639 |         # signifies end of table; now convert them to html table
640 |         if heavy_minus_tracker == 31:
641 |             # convert Bird's Eye view of A(G)I markdown to html table
642 |             agi_nn_html = markdown.markdown("".join(agi_nn), extensions=['markdown.extensions.tables'])
643 |             agi_nn_html = agi_nn_html.replace('<table>', '<table id="aginn">')  # center align table
644 |             # remove underline in url links
645 |             agi_nn_html = agi_nn_html.replace('">', '" style="text-decoration:none">')
646 |             # print(agi_nn_html)
647 |             all_lines.append(agi_nn_html)
648 | 
649 |             # navigation to top
650 |             all_lines.append(add_navigation_button())
651 |             all_lines.append("<hr>")
652 | 
653 |     # write everything to desired html file
654 |     for line in all_lines:
655 |         f.write(line)
656 | 
657 |     # write footer info
658 |     f.write(''.join(html_foot))
659 | 


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