“机器学习是近20多年兴起的一门多领域交叉学科,涉及概率论、统计学、逼近论、凸分析、算法复杂度理论等多门学科。机器学习理论主要是设计和分析一些让 可以自动“ 学习”的算法。机器学习算法是一类从数据中自动分析获得规律,并利用规律对未知数据进行预测的算法。因为学习算法中涉及了大量的统计学理论,机器学习与统计推断学联系尤为密切,也被称为统计学习理论。算法设计方面,机器学习理论关注可以实现的,行之有效的学习算法。很多 推论问题属于 无程序可循难度,所以部分的机器学习研究是开发容易处理的近似算法。”

——中文维基百科

机器学习课程 专知搜集

  1. cs229 机器学习 吴恩达
  2. 台大 李宏毅 机器学习
  3. 爱丁堡大学 机器学习与模式识别
  4. Courses on machine learning
  5. CSC2535 -- Spring 2013 Advanced Machine Learning
  6. Stanford CME 323: Distributed Algorithms and Optimization
  7. University at Buffalo CSE574: Machine Learning and Probabilistic Graphical Models Course
  8. Stanford CS229: Machine Learning Autumn 2015
  9. Stanford / Winter 2014-2015 CS229T/STATS231: Statistical Learning Theory
  10. CMU Fall 2015 10-715: Advanced Introduction to Machine Learning
  11. 2015 Machine Learning Summer School: Convex Optimization Short Course
  12. STA 4273H [Winter 2015]: Large Scale Machine Learning
  13. University of Oxford: Machine Learning: 2014-2015
  14. Computer Science 294: Practical Machine Learning [Fall 2009]
  15. instructor: Michael Jordan
  16. homepage: [https://www.cs.berkeley.edu/~jordan/courses/294-fall09/https://www.cs.berkeley.edu/~jordan/courses/294-fall09/][]
  17. Statistics, Probability and Machine Learning Short Course
  18. Statistical Learning
  19. Machine learning courses online
  20. Build Intelligent Applications: Master machine learning fundamentals in five hands-on courses
  21. Machine Learning
  22. Princeton Computer Science 598D: Overcoming Intractability in Machine Learning
  23. Princeton Computer Science 511: Theoretical Machine Learning
  24. MACHINE LEARNING FOR MUSICIANS AND ARTISTS
  25. CMSC 726: Machine Learning
  26. MIT: 9.520: Statistical Learning Theory and Applications, Fall 2015
  27. CMU: Machine Learning: 10-701/15-781, Spring 2011
  28. NLA 2015 course material
  29. CS 189/289A: Introduction to Machine Learning[with videos]
  30. An Introduction to Statistical Machine Learning Spring 2014 [for ACM Class]
  31. CS 159: Advanced Topics in Machine Learning [Spring 2016]
  32. Advanced Statistical Computing [Vanderbilt University]
  33. Stanford CS229: Machine Learning Spring 2016
  34. Machine Learning: 2015-2016
  35. CS273a: Introduction to Machine Learning
  36. Machine Learning CS-433
  37. Machine Learning Introduction: A machine learning course using Python, Jupyter Notebooks, and OpenML
  38. Advanced Introduction to Machine Learning
  39. STA 4273H [Winter 2015]: Large Scale Machine Learning
  40. Statistical Learning Theory and Applications [MIT]
  41. Regularization Methods for Machine Learning
  42. Convex Optimization: Spring 2015
  43. CMU: Probabilistic Graphical Models [10-708, Spring 2014]
  44. Advanced Optimization and Randomized Methods
  45. Machine Learning for Robotics and Computer Vision
  46. Statistical Machine Learning
  47. Probabilistic Graphical Models [10-708, Spring 2016]

数学基础

Calculus

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

Linear Algebra

  1. Khan Academy Linear Algebra
  2. Linear Algebra MIT 目前最好的线性代数课程

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/]
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