└── README.md /README.md: -------------------------------------------------------------------------------- 1 | # Machine Learning and Computer Vision 2 | ## General ML 3 | * [MIT Artificial Intelligence](https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-034-artificial-intelligence-fall-2010/index.htm) 4 | * [Fast.ai MOOC](http://www.fast.ai/) 5 | * [CMU Deep Learning](http://deeplearning.cs.cmu.edu/) ([youtube](https://www.youtube.com/channel/UC8hYZGEkI2dDO8scT8C5UQA/videos)) 6 | * [UBC ML with Nando de Freitas](https://www.cs.ubc.ca/~nando/540-2013/lectures.html) 7 | * [UofT Neural Networks with Geoffrey Hinton](https://www.coursera.org/learn/neural-networks/home/welcome) 8 | * [Coursera DL series with Andrew Ng](https://www.coursera.org/specializations/deep-learning) 9 | * [Coursera Advanced DL series](https://www.coursera.org/specializations/aml) 10 | * [Coursera & University of Washington ML series](https://www.coursera.org/specializations/machine-learning) 11 | * [Coursera PGM with Daphne Koller](https://www.coursera.org/specializations/probabilistic-graphical-models) 12 | * [Book: the Deep Learning Book](https://www.amazon.com/Deep-Learning-Adaptive-Computation-Machine/dp/0262035618/ref=sr_1_3?s=books&ie=UTF8&qid=1539010049&sr=1-3&keywords=the+deep+learning+book) 13 | 14 | ## Information Theory 15 | * [Cambridge with David Mackay](http://videolectures.net/course_information_theory_pattern_recognition/) ([youtube](https://www.youtube.com/playlist?list=PLruBu5BI5n4aFpG32iMbdWoRVAA-Vcso6)) 16 | * [Book: Information Theory, Inference and Learning Algorithms by David Mackay](https://www.amazon.com/Information-Theory-Inference-Learning-Algorithms/dp/0521642981/ref=sr_1_1?s=books&ie=UTF8&qid=1539010121&sr=1-1&keywords=information+theory+david) 17 | 18 | ## Computer Vision 19 | * [Stanford CS231n with Fei Fei Li](http://cs231n.stanford.edu/2017/) 20 | * [UCF](https://www.youtube.com/playlist?list=PLd3hlSJsX_ImKP68wfKZJVIPTd8Ie5u-9) 21 | * [Georgia Tech & Udacity](https://www.udacity.com/course/introduction-to-computer-vision--ud810) 22 | * [Princeton Robotics](https://www.coursera.org/learn/robotics-perception/home/welcome) 23 | * [book: Computer Vision: Algorithms and Applications](https://www.amazon.com/Computer-Vision-Algorithms-Applications-Science/dp/1848829345) 24 | * [book: Computer Vision: Models, Learning, and Inference](https://www.amazon.com/Computer-Vision-Learning-Inference-2012-06-18/dp/B01N7MROUQ) 25 | 26 | ## Natural Language Processing 27 | * [Stanford CS224n](http://web.stanford.edu/class/cs224n/archive/WWW_1617/index.html) 28 | 29 | ## Reinforcement Learning 30 | * [UC Berkeley Sergey Levine](http://rail.eecs.berkeley.edu/deeprlcourse/) 31 | * [UC Berkeley Bootcamp](https://sites.google.com/view/deep-rl-bootcamp/lectures) 32 | * [UCL David Silver](http://www0.cs.ucl.ac.uk/staff/d.silver/web/Teaching.html) 33 | * [Book: RL by Richard Sutton](http://incompleteideas.net/book/bookdraft2017nov5.pdf) 34 | * [Denny Britz github](https://github.com/dennybritz/reinforcement-learning) 35 | 36 | # Math 37 | ## Linear Algebra 38 | * [MIT Linear Algebra with Gibert Strang](https://ocw.mit.edu/courses/mathematics/18-06sc-linear-algebra-fall-2011/) 39 | * [Book: Linear Algebra and its Applications](https://www.amazon.com/Linear-Algebra-Its-Applications-4th/dp/0030105676) 40 | 41 | ## Probability 42 | * [MIT Intro to Probability](https://ocw.mit.edu/resources/res-6-012-introduction-to-probability-spring-2018/index.htm) 43 | * [MIT Probabilistic Systems Analysis and Applied Probability](https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-041-probabilistic-systems-analysis-and-applied-probability-fall-2010/) 44 | * [MIT Discreate Stochastic Processes](https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-262-discrete-stochastic-processes-spring-2011/index.htm) 45 | 46 | ## Calculus 47 | * [Single Variable Calculus](https://ocw.mit.edu/courses/mathematics/18-01sc-single-variable-calculus-fall-2010/) 48 | * [Multivariable Calculus](https://ocw.mit.edu/courses/mathematics/18-02sc-multivariable-calculus-fall-2010/) 49 | 50 | ## Convex Optimization 51 | * [CMU Convex Optimization](http://www.stat.cmu.edu/~ryantibs/convexopt/) 52 | * [Stanford with Stephen Boyd](https://www.youtube.com/playlist?list=PL3940DD956CDF0622) 53 | 54 | 55 | # Computer Science 56 | ## Basic 57 | * [Stanford C++ Programming](https://web.stanford.edu/class/archive/cs/cs106b/cs106b.1162/index.shtml) 58 | * [book: The C++ Programming Language](https://www.amazon.com/C-Programming-Language-4th/dp/0321563840) 59 | * [book: Exceptional C++](https://www.amazon.com/Exceptional-Engineering-Programming-Problems-Solutions/dp/0201615622/ref=sr_1_1?ie=UTF8&qid=1539011292&sr=8-1&keywords=exceptional+c%2B%2B) 60 | * [book: Python Cookbook](https://www.amazon.com/Python-Cookbook-Third-David-Beazley/dp/144934037x7) 61 | 62 | ## Algorithms 63 | * [MIT Intro to Algorithms with Erik Demaine](https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-006-introduction-to-algorithms-fall-2011/) 64 | * [MIT Design and Analysis of Algorithms with Erik Demaine](https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-046j-design-and-analysis-of-algorithms-spring-2015/index.htm) 65 | * [MIT Advanced Data Structures with Erik Demaine](https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-851-advanced-data-structures-spring-2012/index.htm) 66 | * [MIT Algorithmic Lower Bounds with Erik Demaine](https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-890-algorithmic-lower-bounds-fun-with-hardness-proofs-fall-2014/) 67 | * [MIT Geometric Folding Algorithms with Erik Demaine](https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-849-geometric-folding-algorithms-linkages-origami-polyhedra-fall-2012/) 68 | * [Harvard Advanced Algorithms](http://people.seas.harvard.edu/~minilek/cs224/fall14/index.html) 69 | * [Princeton Algorithms part I](https://www.coursera.org/learn/algorithms-part1) 70 | * [Princeton Algorithms part II](https://www.coursera.org/learn/algorithms-part2) 71 | * [MIT Programming for the Puzzled](https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-s095-programming-for-the-puzzled-january-iap-2018/index.htm) 72 | * [book: Algorithms Sedgewick](https://www.amazon.com/Algorithms-Algorithms_4-Robert-Sedgewick-ebook/dp/B004P8J1NA) 73 | * [book: Algorithms CLRS](https://www.amazon.com/Introduction-Algorithms-Press-Thomas-Cormen-ebook/dp/B007CNRCAO/ref=sr_1_4?s=digital-text&ie=UTF8&qid=1539010492&sr=1-4&keywords=algorithms) 74 | 75 | ## Computer Systems 76 | * [CMU Intro to Computer Systems](http://www.cs.cmu.edu/~./213/) 77 | * [Udacity High Performance Computing](https://classroom.udacity.com/courses/ud281) 78 | * [book: Computer Systems: A programmer's perspective](https://www.amazon.com/Computer-Systems-Programmers-Randal-Bryant-ebook/dp/B008VIXMWQ) 79 | 80 | ## Parallel Computing 81 | * [Udacity Intro to Parallel Programming](https://classroom.udacity.com/courses/cs344) 82 | * [CMU Prallel Computer Architecture and Programming](http://15418.courses.cs.cmu.edu/spring2016/) 83 | * [book: CUDA by Example](https://www.amazon.com/CUDA-Example-Introduction-General-Purpose-Programming/dp/0131387685/ref=sr_1_1?ie=UTF8&qid=1539011183&sr=8-1&keywords=cuda) 84 | 85 | ## OS 86 | * [Udacity OS](https://www.udacity.com/course/introduction-to-operating-systems--ud923) 87 | * [Udacity Advanced OS](https://www.udacity.com/course/advanced-operating-systems--ud189) 88 | * [Online Book: OS three easy pieces](http://pages.cs.wisc.edu/~remzi/OSTEP/) 89 | 90 | ## Compilers 91 | * [MIT Computer Language Engineering](https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-035-computer-language-engineering-sma-5502-fall-2005/index.htm) 92 | * [Coursera & Stanford Compilers](https://www.bilibili.com/video/av16607911/) 93 | * [Advanced Topics in Programming Languages](https://www.cs.cmu.edu/~janh/courses/ra16/) 94 | 95 | ## Databases 96 | * [Coursera & Stanford with Jennifer Widom](https://www.youtube.com/playlist?list=PLroEs25KGvwzmvIxYHRhoGTz9w8LeXek0) 97 | * [CMU Advanced Database Systems](https://15721.courses.cs.cmu.edu/spring2018/) 98 | 99 | 100 | # Blockchain 101 | * [Coursera Blockchain](https://www.coursera.org/learn/cryptocurrency/home/welcome) 102 | 103 | 104 | # Learning to Learn 105 | * [Coursera Learning how to learn](https://www.coursera.org/learn/learning-how-to-learn/home/welcome) 106 | * [Coursera Mindshift](https://www.coursera.org/learn/mindshift/home/welcome) 107 | --------------------------------------------------------------------------------