斯坦福大学(Andrew Ng)机器学习课程讲义

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 

 

 

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