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
转自：http://blog.163.com/bioinfor_cnu/blog/static/194462237201181411551651/