“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

知识荟萃

机器学习课程 专知搜集

  1. 机器学习视频课程汇集: ML入门、数据挖掘、数据科学、 概率图模型、深度学习、强化学习、高级机器学习、基于ML的自然语言处理与计算机视觉、时序分析
  2. cs229 机器学习 吴恩达
  3. 台大 李宏毅 机器学习
  4. 爱丁堡大学 机器学习与模式识别
  5. Courses on machine learning
  6. CSC2535 -- Spring 2013 Advanced Machine Learning
  7. Stanford CME 323: Distributed Algorithms and Optimization
  8. University at Buffalo CSE574: Machine Learning and Probabilistic Graphical Models Course
  9. Stanford CS229: Machine Learning Autumn 2015
  10. Stanford / Winter 2014-2015 CS229T/STATS231: Statistical Learning Theory
  11. CMU Fall 2015 10-715: Advanced Introduction to Machine Learning
  12. 2015 Machine Learning Summer School: Convex Optimization Short Course
  13. STA 4273H [Winter 2015]: Large Scale Machine Learning
  14. University of Oxford: Machine Learning: 2014-2015
  15. 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]

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/]

精品内容

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