机器学习课程（第三方链接汇总）

0. cs229 机器学习 吴恩达
   • [http://cs229.stanford.edu/syllabus.html]
1. 台大 李宏毅 机器学习
   • [http://speech.ee.ntu.edu.tw/~tlkagk/courses.html]
2. 爱丁堡大学 机器学习与模式识别
   • [http://www.inf.ed.ac.uk/teaching/courses/mlpr/2017/notes/]
3. Courses on machine learning
   • [http://homepages.inf.ed.ac.uk/rbf/IAPR/researchers/MLPAGES/mlcourses.htm][http://homepages.inf.ed.ac.uk/rbf/IAPR/researchers/MLPAGES/mlcourses.htm]
4. CSC2535 -- Spring 2013 Advanced Machine Learning
   • instructor: by Hinton, University of Toronto
   • homepage: [http://www.cs.toronto.edu/~hinton/csc2535/][http://www.cs.toronto.edu/~hinton/csc2535/]
5. Stanford CME 323: Distributed Algorithms and Optimization
   • [http://stanford.edu/~rezab/dao/][http://stanford.edu/~rezab/dao/]
6. University at Buffalo CSE574: Machine Learning and Probabilistic Graphical Models Course
   • [http://www.cedar.buffalo.edu/~srihari/CSE574/][http://www.cedar.buffalo.edu/~srihari/CSE574/]
7. Stanford CS229: Machine Learning Autumn 2015
   • instructor: Andrew Ng
   • homepage: [http://cs229.stanford.edu/][http://cs229.stanford.edu/]
   • project page: [http://cs229.stanford.edu/projects2015.html][http://cs229.stanford.edu/projects2015.html]
8. Stanford / Winter 2014-2015 CS229T/STATS231: Statistical Learning Theory
   • instructor: Percy Liang
   • homepage: [http://web.stanford.edu/class/cs229t/][http://web.stanford.edu/class/cs229t/]
   • lecture notes: [http://web.stanford.edu/class/cs229t/notes.pdf][http://web.stanford.edu/class/cs229t/notes.pdf]
9. CMU Fall 2015 10-715: Advanced Introduction to Machine Learning
   • instructor: Alex Smola, Barnabas Poczos
   • homepage: [http://www.cs.cmu.edu/~bapoczos/Classes/ML10715_2015Fall/][http://www.cs.cmu.edu/~bapoczos/Classes/ML10715_2015Fall/]
   • video: [http://pan.baidu.com/s/1qWvcsUS][http://pan.baidu.com/s/1qWvcsUS]
10. 2015 Machine Learning Summer School: Convex Optimization Short Course
    • instructor: S. Boyd and S. Diamond
    • Lecture slides and IPython notebooks: [https://stanford.edu/~boyd/papers/cvx_short_course.html][https://stanford.edu/~boyd/papers/cvx_short_course.html]
11. STA 4273H [Winter 2015]: Large Scale Machine Learning
    • [http://www.cs.toronto.edu/~rsalakhu/STA4273_2015/][http://www.cs.toronto.edu/~rsalakhu/STA4273_2015/]
12. University of Oxford: Machine Learning: 2014-2015
    • homepage: [https://www.cs.ox.ac.uk/people/nando.defreitas/machinelearning/][https://www.cs.ox.ac.uk/people/nando.defreitas/machinelearning/]
    • course materials: [https://www.cs.ox.ac.uk/people/nando.defreitas/machinelearning/][https://www.cs.ox.ac.uk/people/nando.defreitas/machinelearning/]
    • lectures: [http://pan.baidu.com/s/1bndbxJh#path=%252FDeep%2520Learning%2520Lectures][http://pan.baidu.com/s/1bndbxJh#path=%252FDeep%2520Learning%2520Lectures]
    • github: [https://github.com/oxford-cs-ml-2015/][https://github.com/oxford-cs-ml-2015/]
13. Computer Science 294: Practical Machine Learning [Fall 2009]

• instructor: Michael Jordan
• homepage: [https://www.cs.berkeley.edu/~jordan/courses/294-fall09/][https://www.cs.berkeley.edu/~jordan/courses/294-fall09/]

15. Statistics, Probability and Machine Learning Short Course
    • homepage: [http://www-staff.it.uts.edu.au/~ydxu/statistics.htm][http://www-staff.it.uts.edu.au/~ydxu/statistics.htm]
    • youku: [http://i.youku.com/u/UMzIzNDgxNTg5Ng][http://i.youku.com/u/UMzIzNDgxNTg5Ng]
    • youbube: [https://www.youtube.com/playlist?list=PLFze15KrfxbF0n1zTNoFIaDpxnSyfgNgc]
    • [https://www.youtube.com/playlist?list=PLFze15KrfxbF0n1zTNoFIaDpxnSyfgNgc]
16. Statistical Learning
    • [https://lagunita.stanford.edu/courses/HumanitiesScience/StatLearning/Winter2014/about]
    • [https://lagunita.stanford.edu/courses/HumanitiesScience/StatLearning/Winter2014/about]
17. Machine learning courses online
    • [http://fastml.com/machine-learning-courses-online/][http://fastml.com/machine-learning-courses-online/]
18. Build Intelligent Applications: Master machine learning fundamentals in five hands-on courses
    • [https://www.coursera.org/specializations/machine-learning]
    • [https://www.coursera.org/specializations/machine-learning]
19. Machine Learning
    • [http://www.cs.ubc.ca/~nando/540-2013/lectures.html]
    • [http://www.cs.ubc.ca/~nando/540-2013/lectures.html]
20. Princeton Computer Science 598D: Overcoming Intractability in Machine Learning
    • [http://www.cs.princeton.edu/courses/archive/spring15/cos598D/]
    • [http://www.cs.princeton.edu/courses/archive/spring15/cos598D/]
21. Princeton Computer Science 511: Theoretical Machine Learning
    • instructor: Rob Schapire
    • homepage: [http://www.cs.princeton.edu/courses/archive/spring14/cos511/schedule.html]
    • [http://www.cs.princeton.edu/courses/archive/spring14/cos511/schedule.html]
22. MACHINE LEARNING FOR MUSICIANS AND ARTISTS
    • [https://www.kadenze.com/courses/machine-learning-for-musicians-and-artists/info][https://www.kadenze.com/courses/machine-learning-for-musicians-and-artists/info]
23. CMSC 726: Machine Learning
    • homepage: [http://www.cbcb.umd.edu/~hcorrada/PML/index.html]
    • [http://www.cbcb.umd.edu/~hcorrada/PML/index.html]
24. MIT: 9.520: Statistical Learning Theory and Applications, Fall 2015
    • [http://www.mit.edu/~9.520/fall15/][http://www.mit.edu/~9.520/fall15/]
25. CMU: Machine Learning: 10-701/15-781, Spring 2011
    • instructor: Tom Mitchell
    • homepage: [http://www.cs.cmu.edu/~tom/10701_sp11/][http://www.cs.cmu.edu/~tom/10701_sp11/]
    • lectures: [http://www.cs.cmu.edu/~tom/10701_sp11/lectures.shtml][http://www.cs.cmu.edu/~tom/10701_sp11/lectures.shtml]
26. NLA 2015 course material
    • ipn: [http://nbviewer.jupyter.org/github/Bihaqo/nla2015/blob/master/table_of_contents.ipynb][http://nbviewer.jupyter.org/github/Bihaqo/nla2015/blob/master/table_of_contents.ipynb]
27. CS 189/289A: Introduction to Machine Learning[with videos]
    • [http://www.cs.berkeley.edu/~jrs/189/qdaaniso3d.png]
    • homepage: [http://www.cs.berkeley.edu/~jrs/189/][http://www.cs.berkeley.edu/~jrs/189/]
28. An Introduction to Statistical Machine Learning Spring 2014 [for ACM Class]
    • [http://bcmi.sjtu.edu.cn/log/courses/ml_2014_spring_acm.html]
    • [http://bcmi.sjtu.edu.cn/log/courses/ml_2014_spring_acm.html]
29. CS 159: Advanced Topics in Machine Learning [Spring 2016]
    • intro: Online Learning, Multi-Armed Bandits, Active Learning, Human-in-the-Loop Learning, Reinforcement Learning
    • instructor: Yisong Yue
    • homepage: [http://www.yisongyue.com/courses/cs159/][http://www.yisongyue.com/courses/cs159/]
30. Advanced Statistical Computing [Vanderbilt University]
    • intro: Course covers numerical optimization, Markov Chain Monte Carlo [MCMC], Metropolis-Hastings, Gibbs sampling, estimation-maximization [EM] algorithms, data augmentation algorithms with applications for model fitting and techniques for dealing with missing data
    • homepage: [http://stronginference.com/Bios8366/][http://stronginference.com/Bios8366/]
    • lecture: [http://stronginference.com/Bios8366/lectures.html][http://stronginference.com/Bios8366/lectures.html]
    • github: [https://github.com/fonnesbeck/Bios8366][https://github.com/fonnesbeck/Bios8366]
31. Stanford CS229: Machine Learning Spring 2016
    • instructor: John Duchi
    • homepage: [http://cs229.stanford.edu/][http://cs229.stanford.edu/]
    • materials: [http://cs229.stanford.edu/materials.html][http://cs229.stanford.edu/materials.html]
32. Machine Learning: 2015-2016
    • homepage: [https://www.cs.ox.ac.uk/teaching/courses/2015-2016/ml/][https://www.cs.ox.ac.uk/teaching/courses/2015-2016/ml/]
    • materials: [http://www.cs.ox.ac.uk/people/varun.kanade/teaching/ML-HT2016/index.html][http://www.cs.ox.ac.uk/people/varun.kanade/teaching/ML-HT2016/index.html]
33. CS273a: Introduction to Machine Learning
    • homepage: [http://sli.ics.uci.edu/Classes/2015W-273a][http://sli.ics.uci.edu/Classes/2015W-273a]
    • youtube: [https://www.youtube.com/playlist?list=PLaXDtXvwY-oDvedS3f4HW0b4KxqpJ_imw][https://www.youtube.com/playlist?list=PLaXDtXvwY-oDvedS3f4HW0b4KxqpJ_imw]
    • course notes: [http://sli.ics.uci.edu/Classes-CS178-Notes/Classes-CS178-Notes][http://sli.ics.uci.edu/Classes-CS178-Notes/Classes-CS178-Notes]
34. Machine Learning CS-433
    • homepage: [http://mlo.epfl.ch/page-136795.html][http://mlo.epfl.ch/page-136795.html]
    • github: [https://github.com/epfml/ML_course][https://github.com/epfml/ML_course]
35. Machine Learning Introduction: A machine learning course using Python, Jupyter Notebooks, and OpenML
    • [https://github.com/joaquinvanschoren/ML-course][https://github.com/joaquinvanschoren/ML-course]
36. Advanced Introduction to Machine Learning
    • homepage: [http://www.cs.cmu.edu/~bapoczos/Classes/ML10715_2015Fall/index.html][http://www.cs.cmu.edu/~bapoczos/Classes/ML10715_2015Fall/index.html]
    • video: [https://www.youtube.com/playlist?list=PL4DwY1suLMkcu-wytRDbvBNmx57CdQ2pJ&jct=q4qVgISGxJql7TlE6eSLKa8Wwci8SA][https://www.youtube.com/playlist?list=PL4DwY1suLMkcu-wytRDbvBNmx57CdQ2pJ&jct=q4qVgISGxJql7TlE6eSLKa8Wwci8SA]
37. STA 4273H [Winter 2015]: Large Scale Machine Learning
    • [http://www.cs.toronto.edu/~rsalakhu/STA4273_2015/][http://www.cs.toronto.edu/~rsalakhu/STA4273_2015/]
38. Statistical Learning Theory and Applications [MIT]
    • homepage: [http://www.mit.edu/~9.520/fall15/index.html][http://www.mit.edu/~9.520/fall15/index.html]
    • video: [https://www.youtube.com/playlist?list=PLyGKBDfnk-iDj3FBd0Avr_dLbrU8VG73O][https://www.youtube.com/playlist?list=PLyGKBDfnk-iDj3FBd0Avr_dLbrU8VG73O]
39. Regularization Methods for Machine Learning

• homepage: [http://lcsl.mit.edu/courses/regml/regml2016/][http://lcsl.mit.edu/courses/regml/regml2016/]
• video: [https://www.youtube.com/playlist?list=PLbF0BXX_6CPJ20Gf_KbLFnPWjFTvvRwCO][https://www.youtube.com/playlist?list=PLbF0BXX_6CPJ20Gf_KbLFnPWjFTvvRwCO]

40. Convex Optimization: Spring 2015
    • homepage: [http://www.stat.cmu.edu/~ryantibs/convexopt-S15/][http://www.stat.cmu.edu/~ryantibs/convexopt-S15/]
    • video: [https://www.youtube.com/playlist?list=PLjbUi5mgii6BZBhJ9nW7eydgycyCOYeZ6][https://www.youtube.com/playlist?list=PLjbUi5mgii6BZBhJ9nW7eydgycyCOYeZ6]
41. CMU: Probabilistic Graphical Models [10-708, Spring 2014]
    • instructor: Eric Xing
    • homepage: [http://www.cs.cmu.edu/~epxing/Class/10708/][http://www.cs.cmu.edu/~epxing/Class/10708/]
    • lecture: [http://www.cs.cmu.edu/~epxing/Class/10708-14/lecture.html][http://www.cs.cmu.edu/~epxing/Class/10708-14/lecture.html]
42. Advanced Optimization and Randomized Methods
    • instructor: A. Smola, S. Sra
    • homepage: [http://www.cs.cmu.edu/~suvrit/teach/index.html][http://www.cs.cmu.edu/~suvrit/teach/index.html]
43. Machine Learning for Robotics and Computer Vision
    • homepage: [http://vision.in.tum.de/teaching/ws2013/ml_ws13][http://vision.in.tum.de/teaching/ws2013/ml_ws13]
    • video: [https://www.youtube.com/watch?v=QZmZFeZxEKI&list=PLTBdjV_4f-EIiongKlS9OKrBEp8QR47Wl][https://www.youtube.com/watch?v=QZmZFeZxEKI&list=PLTBdjV_4f-EIiongKlS9OKrBEp8QR47Wl]
44. Statistical Machine Learning
    • homepage: [http://www.stat.cmu.edu/~larry/=sml/][http://www.stat.cmu.edu/~larry/=sml/]
    • video: [https://www.youtube.com/playlist?list=PLTB9VQq8WiaCBK2XrtYn5t9uuPdsNm7YE][https://www.youtube.com/playlist?list=PLTB9VQq8WiaCBK2XrtYn5t9uuPdsNm7YE]
    • mirror: [http://pan.baidu.com/s/1eSuJ1Nc][http://pan.baidu.com/s/1eSuJ1Nc]
45. Probabilistic Graphical Models [10-708, Spring 2016]
    • [http://www.cs.cmu.edu/~epxing/Class/10708-16/lecture.html]
    • [http://www.cs.cmu.edu/~epxing/Class/10708-16/lecture.html]

数学基础

Calculus

1. Khan Academy Calculus [https://www.khanacademy.org/math/calculus-home]

Linear Algebra

1. Khan Academy Linear Algebra
   • https://www.khanacademy.org/math/linear-algebra
2. Linear Algebra MIT 目前最好的线性代数课程
   • http://ocw.mit.edu/courses/mathematics/18-06-linear-algebra-spring-2010/video-lectures/lecture-21-eigenvalues-and-eigenvectors/)

Statistics and probability

1. edx Introduction to Statistics [https://www.edx.org/course/introduction-statistics-descriptive-uc-berkeleyx-stat2-1x]
2. edx Probability [https://www.edx.org/course/introduction-statistics-probability-uc-berkeleyx-stat2-2x]
3. An exploration of Random Processes for Engineers [http://www.ifp.illinois.edu/~hajek/Papers/randomprocDec11.pdf]
4. Information Theory [http://colah.github.io/posts/2015-09-Visual-Information/]