斯坦福大学机器学习课程讲义

教师：Andrew Ng<wbr></wbr>

http://see.stanford.edu/see/courseinfo.aspx?coll=348ca38a-3a6d-4052-937d-cb017338d7b1

http://www.stanford.edu/class/cs229/materials.html

Lecture notes 1 (ps)(pdf) Supervised Learning, Discriminative Algorithms
Lecture notes 2 (ps)(pdf) Generative Algorithms
Lecture notes 3 (ps)(pdf) Support Vector Machines
Lecture notes 4 (ps)(pdf) Learning Theory
Lecture notes 5 (ps)(pdf) Regularization and Model Selection
Lecture notes 6 (ps)(pdf) Online Learning and the Perceptron Algorithm. (optional reading)
Lecture notes 7a (ps)(pdf) Unsupervised Learning, k-means clustering.
Lecture notes 7b (ps)(pdf) Mixture of Gaussians
Lecture notes 8 (ps)(pdf) The EM Algorithm
Lecture notes 9 (ps)(pdf) Factor Analysis
Lecture notes 10 (ps)(pdf) Principal Components Analysis
Lecture notes 11 (ps)(pdf) Independent Components Analysis
Lecture notes 12 (ps)(pdf) Reinforcement Learning and Control