Pattern Recognition and Machine Learning
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
9. Mixture Models and EM
11. Sampling Methods
12. Coninuous Latent Variables
13. Sequential Data
14. Combining Models
