├── DP-Richard-Xu.md ├── EM-Richard-Xu.md ├── HMM-Richard-Xu.md ├── MCMC-Richard-Xu.md ├── README.md ├── Resources ├── DP-Richard-Xu │ └── Lecture-Notes.pdf ├── EM-Richard-Xu │ └── Lecture-Notes.pdf ├── HMM-Richard-Xu │ └── Lecture-Notes.pdf ├── MCMC-Richard-Xu │ └── Lecture-Notes.pdf └── VI-Richard-Xu │ └── Lecture-Notes.pdf └── VI-Richard-Xu.md /DP-Richard-Xu.md: -------------------------------------------------------------------------------- 1 | # Non-parametric Bayes & applications (Richard Xu) 2 | * [DP (Richard Xu)](DP-Richard-Xu.md) - Dirichlet Process, Hieratical Dirichlet Process, HDP-HMM, Indian Buffet Process, and applications of DP to relational models. 3 | 4 | ## Lecture Videos 5 | * [DP - Youku](http://v.youku.com/v_show/id_XMTM3NDY0MDkxNg==.html?f=26896501) 6 | 7 | ## Lecture Notes 8 | * [Notes](Resources/DP-Richard-Xu/Lecture-Notes.pdf) 9 | * [External Links](http://www-staff.it.uts.edu.au/~ydxu/ml_course/non_parametrics.pdf) 10 | 11 | ## Lecture Code/Demos 12 | 13 | 14 | ## Lecture References 15 | 16 | 17 | ## Copyright 18 | * Resources provided by [Dr. Richard Xu](http://www-staff.it.uts.edu.au/~ydxu/index.htm). 19 | * Personal Notes provided by Ziyang Qi, redistribution requires authorization. 20 | 21 | ## Personsal Notes 22 | * TBD 23 | -------------------------------------------------------------------------------- /EM-Richard-Xu.md: -------------------------------------------------------------------------------- 1 | # Expectation Maximization(Richard Xu) 2 | Proof of convergence for E-M, examples of E-M through Gaussian Mixture Model 3 | 4 | ## Lecture Videos 5 | * [Youtube](https://www.youtube.com/playlist?list=PLyAft-JyjIYpno8IfZZS0mnxD5TYZ6BIc) 6 | 7 | ## Lecture Notes 8 | * [Notes](Resources/EM-Richard-Xu/Lecture-Notes.pdf) 9 | * [External Links](http://www-staff.it.uts.edu.au/~ydxu/ml_course/em.pdf) 10 | 11 | ## Lecture Code/Demos 12 | * [Geometrically-constrained balloon fitting for multiple connected ellipses](http://www-staff.it.uts.edu.au/~ydxu/code/balloon/) 13 | 14 | ## Lecture References 15 | * [Kemp, M and Xu, R. Y. D (2015) Geometrically-constrained balloon fitting for multiple connected ellipses, Pattern Recognition, 48 (2015) pp. 2198 - 2208, SSN: 0031-3203. (ERA Rank A* Journal ISI IF: 2.584) ](http://www-staff.it.uts.edu.au/~ydxu/papers/kemp_xu_balloon.pdf) 16 | 17 | ## Copyright 18 | * Resources provided by [Dr. Richard Xu](http://www-staff.it.uts.edu.au/~ydxu/index.htm). 19 | * Personal Notes provided by Ziyang Qi, redistribution requires authorization. 20 | 21 | ## Personsal Notes 22 | * TBD 23 | -------------------------------------------------------------------------------- /HMM-Richard-Xu.md: -------------------------------------------------------------------------------- 1 | # Expectation Maximization(Richard Xu) 2 | Derivations for Kalman Filter and Hidden Markov Model 3 | 4 | ## Lecture Videos 5 | * [HMM Youtube](https://www.youtube.com/playlist?list=PLyAft-JyjIYoc9LN241WKqLPuggfSBBpt) 6 | * [Kalman Filter Youku](http://v.youku.com/v_show/id_XMTM2ODU1MzMzMg==.html?f=26896484) 7 | 8 | ## Lecture Notes 9 | * [Notes](Resources/HMM-Richard-Xu/Lecture-Notes.pdf) 10 | * [External Links](http://www-staff.it.uts.edu.au/~ydxu/ml_course/dynamic_model.pdf) 11 | 12 | ## Lecture Code/Demos 13 | 14 | * [计算机视觉演示：PTZ camera control (Youku)](http://v.youku.com/v_show/id_XMTM1NDM4OTg0OA==.html?from=y1.7-1.2) 15 | 16 | ## Lecture References 17 | 18 | 19 | ## Copyright 20 | * Resources provided by [Dr. Richard Xu](http://www-staff.it.uts.edu.au/~ydxu/index.htm). 21 | * Personal Notes provided by Ziyang Qi, redistribution requires authorization. 22 | 23 | ## Personsal Notes 24 | * TBD 25 | -------------------------------------------------------------------------------- /MCMC-Richard-Xu.md: -------------------------------------------------------------------------------- 1 | # Markov Chain Monte Carlo(Richard Xu) 2 | Markov Chain Monte Carlo (MCMC), Gibbs, Bootstrap Particle Filter, and Auxiliary Particle Filter 3 | 4 | ## Lecture Videos 5 | * [MCMC - Youtube](https://www.youtube.com/playlist?list=PLyAft-JyjIYq2SLTHO2ptmx-cChbE5GBm) 6 | * [Particle Filter - Youku](http://v.youku.com/v_show/id_XMTM3MTE1Mjk2OA==.html?f=26896488) 7 | 8 | ## Lecture Notes 9 | * [Notes](Resources/MCMC-Richard-Xu/Lecture-Notes.pdf) 10 | * [External Links](http://www-staff.it.uts.edu.au/~ydxu/ml_course/monte_carlo.pdf) 11 | 12 | ## Lecture Code/Demos 13 | 14 | * [视频: 计算机视觉演示: 抢球联动 Whiteboard Scanner (Youku)](http://v.youku.com/v_show/id_XMTM1Njc3MTU3Ng==.html?from=y1.7-1.2) 15 | 16 | ## Lecture References 17 | 18 | 19 | ## Copyright 20 | * Resources provided by [Dr. Richard Xu](http://www-staff.it.uts.edu.au/~ydxu/index.htm). 21 | * Personal Notes provided by Ziyang Qi, redistribution requires authorization. 22 | 23 | ## Personsal Notes 24 | * TBD 25 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Machine-Learning-Notes 2 | ----------- 3 | # By Origin 4 | 5 | ## Machine Learning (Andrew Ng) 6 | * [Machine Learning (Stanford CS 229 - Andrew Ng)](https://www.youtube.com/playlist?list=PLA89DCFA6ADACE599) - provides a broad introduction to machine learning and statistical pattern recognition. Topics include supervised learning, unsupervised learning, learning theory, reinforcement learning and adaptive control. Recent applications of machine learning, such as to robotic control, data mining, autonomous navigation, bioinformatics, speech recognition, and text and web data processing are also discussed. 7 | 8 | ## Machine Learning (mathematicalmonk) 9 | * [Youtube](https://www.youtube.com/playlist?list=PLD0F06AA0D2E8FFBA) 10 | 11 | ## Introductory Applied Machine Learning (IAML) (Victor lavrenko) 12 | * [Youtube](https://www.youtube.com/user/victorlavrenko/playlists?shelf_id=10&sort=dd&view=50) 13 | 14 | ## Intro to Artificial Intelligence (Sebastian Thrun & Peter Norvig) 15 | * [Udacity](https://www.udacity.com/course/intro-to-artificial-intelligence--cs271) 16 | * [Youtube](https://www.youtube.com/playlist?list=PLF236785979DFDDE6) 17 | 18 | ----------- 19 | # By Topics 20 | 21 | ## Expectation Maximization 22 | * [EM (Richard Xu)](EM-Richard-Xu.md) - Proof of convergence for E-M, examples of E-M through Gaussian Mixture Model 23 | * [Machine Learning Lecture 12 (Stanford CS 229 - Andrew Ng)](https://www.youtube.com/watch?v=ZZGTuAkF-Hw) - discusses unsupervised learning in the context of clustering, Jensen's inequality, mixture of Gaussians, and expectation-maximization. 24 | * [Machine Learning (mathematicalmonk) Lecture 16.3](https://www.youtube.com/watch?v=AnbiNaVp3eQ&list=PLD0F06AA0D2E8FFBA&index=116) 25 | * [Lecture 17 of the Introductory Applied Machine Learning (IAML) course - University of Edinburgh, by Victor lavrenko](https://www.youtube.com/playlist?list=PLBv09BD7ez_7beI0_fuE96lSbsr_8K8YD) 26 | * [Intro to Artificial Intelligence (Sebastian Thrun & Peter Norvig) Unit 6](https://www.youtube.com/watch?v=_DhelJs0BFc&list=PL31YDGENcieLqfh7-SeYLOY9QLqAkQUgH) 27 | 28 | ## Hidden Markov Model 29 | * [HMM (Richard Xu)](HMM-Richard-Xu.md) - Derivations for Kalman Filter and Hidden Markov Model 30 | * [Intro to Artificial Intelligence (Sebastian Thrun & Peter Norvig) Unit 11](https://www.youtube.com/watch?v=s5jbwPgheqI&list=PLKG3ExuC02lsnZUJDdOlYJd5CRe3otzq1) 31 | 32 | ## Markov Chain Monte Carlo 33 | * [MCMC (Richard Xu)](MCMC-Richard-Xu.md) - Overview of several Sampling techniques, including Rejection, Adaptive Rejection, Importance, Markov Chain Monte Carlo (MCMC), Gibbs, Bootstrap Particle Filter, and Auxiliary Particle Filter 34 | 35 | ## Variational Inference 36 | * [VI (Richard Xu)](VI-Richard-Xu.md) - Explain Variational Bayes in two parts: non-exponential and exponential family distribution plus stochastic variational inference. 37 | 38 | ## Non-parametric Bayes & applications 39 | * [DP (Richard Xu)](DP-Richard-Xu.md) - Dirichlet Process, Hieratical Dirichlet Process, HDP-HMM, Indian Buffet Process, and applications of DP to relational models. 40 | -------------------------------------------------------------------------------- /Resources/DP-Richard-Xu/Lecture-Notes.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/qizy09/Machine-Learning-Notes/f74b0085ce44fe8c8f66ff9371624b5b5d215eb2/Resources/DP-Richard-Xu/Lecture-Notes.pdf -------------------------------------------------------------------------------- /Resources/EM-Richard-Xu/Lecture-Notes.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/qizy09/Machine-Learning-Notes/f74b0085ce44fe8c8f66ff9371624b5b5d215eb2/Resources/EM-Richard-Xu/Lecture-Notes.pdf -------------------------------------------------------------------------------- /Resources/HMM-Richard-Xu/Lecture-Notes.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/qizy09/Machine-Learning-Notes/f74b0085ce44fe8c8f66ff9371624b5b5d215eb2/Resources/HMM-Richard-Xu/Lecture-Notes.pdf -------------------------------------------------------------------------------- /Resources/MCMC-Richard-Xu/Lecture-Notes.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/qizy09/Machine-Learning-Notes/f74b0085ce44fe8c8f66ff9371624b5b5d215eb2/Resources/MCMC-Richard-Xu/Lecture-Notes.pdf -------------------------------------------------------------------------------- /Resources/VI-Richard-Xu/Lecture-Notes.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/qizy09/Machine-Learning-Notes/f74b0085ce44fe8c8f66ff9371624b5b5d215eb2/Resources/VI-Richard-Xu/Lecture-Notes.pdf -------------------------------------------------------------------------------- /VI-Richard-Xu.md: -------------------------------------------------------------------------------- 1 | # Expectation Maximization(Richard Xu) 2 | Explain Variational Bayes in two parts: non-exponential and exponential family distribution plus stochastic variational inference. 3 | 4 | ## Lecture Videos 5 | * [VI - Youtube](https://www.youtube.com/playlist?list=PLFze15KrfxbF0n1zTNoFIaDpxnSyfgNgc) 6 | 7 | ## Lecture Notes 8 | * [Notes](Resources/VI-Richard-Xu/Lecture-Notes.pdf) 9 | * [External Links](http://www-staff.it.uts.edu.au/~ydxu/ml_course/variational.pdf) 10 | 11 | ## Lecture Code/Demos 12 | 13 | 14 | ## Lecture References 15 | 16 | 17 | ## Copyright 18 | * Resources provided by [Dr. Richard Xu](http://www-staff.it.uts.edu.au/~ydxu/index.htm). 19 | * Personal Notes provided by Ziyang Qi, redistribution requires authorization. 20 | 21 | ## Personsal Notes 22 | * TBD 23 | --------------------------------------------------------------------------------