1. Introduction
2. Probability Distributions
3. Linear Models for Regression
4. Linear Models for Classification
5. Neural Networks
6. Kernel Methods
7. Sparse Kernel Machines
8. Graphical Models
9. Mixture Models and EM
10. Approximate Inference
11. Sampling Methods
12. Coninuous Latent Variables
13. Sequential Data
14. Combining Models
转载于:https://www.cnblogs.com/kuiyuan/archive/2011/08/29/2157366.html
相关资源：Pattern Recognition and Machine Learning. Bishop. 完整版pdf