“機器學習是近20多年興起的一門多領域交叉學科,涉及概率論、統計學、逼近論、凸分析、算法複雜度理論等多門學科。機器學習理論主要是設計和分析一些讓可以自動“學習”的算法。機器學習算法是一類從數據中自動分析獲得規律,並利用規律對未知數據進行預測的算法。因為學習算法中涉及了大量的統計學理論,機器學習與統計推斷學聯係尤為密切,也被稱為統計學習理論。算法設計方麵,機器學習理論關注可以實現的,行之有效的學習算法。很多推論問題屬於無程序可循難度,所以部分的機器學習研究是開發容易處理的近似算法。” ——中文維基百科

機器學習課程 專知搜集

  1. cs229 機器學習 吳恩達
  2. 台大 李宏毅 機器學習
  3. 愛丁堡大學 機器學習與模式識別
  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]
  1. Statistics, Probability and Machine Learning Short Course
  2. Statistical Learning
  3. Machine learning courses online
  4. Build Intelligent Applications: Master machine learning fundamentals in five hands-on courses
  5. Machine Learning
  6. Princeton Computer Science 598D: Overcoming Intractability in Machine Learning
  7. Princeton Computer Science 511: Theoretical Machine Learning
  8. MACHINE LEARNING FOR MUSICIANS AND ARTISTS
  9. CMSC 726: Machine Learning
  10. MIT: 9.520: Statistical Learning Theory and Applications, Fall 2015
  11. CMU: Machine Learning: 10-701/15-781, Spring 2011
  12. NLA 2015 course material
  13. CS 189/289A: Introduction to Machine Learning[with videos]
  14. An Introduction to Statistical Machine Learning Spring 2014 [for ACM Class]
  15. CS 159: Advanced Topics in Machine Learning [Spring 2016]
  16. Advanced Statistical Computing [Vanderbilt University]
  17. Stanford CS229: Machine Learning Spring 2016
  18. Machine Learning: 2015-2016
  19. CS273a: Introduction to Machine Learning
  20. Machine Learning CS-433
  21. Machine Learning Introduction: A machine learning course using Python, Jupyter Notebooks, and OpenML
  22. Advanced Introduction to Machine Learning
  23. STA 4273H [Winter 2015]: Large Scale Machine Learning
  24. Statistical Learning Theory and Applications [MIT]
  25. Regularization Methods for Machine Learning
  1. Convex Optimization: Spring 2015
  2. CMU: Probabilistic Graphical Models [10-708, Spring 2014]
  3. Advanced Optimization and Randomized Methods
  4. Machine Learning for Robotics and Computer Vision
  5. Statistical Machine Learning
  6. Probabilistic Graphical Models [10-708, Spring 2016]

數學基礎

Calculus

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

Linear Algebra

  1. Khan Academy Linear Algebra
  2. Linear Algebra MIT 目前最好的線性代數課程

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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