- Introduction
- Probability Distributions
- Linear Models for Regression
- Linear Models for Classification
- Neural Networks
- Kernel Methods
- Sparse Kernel Machines
- Graphical Models
- Mixture Models and EM
- Approximate Inference
- Sampling Methods
- Continuous Latent Variables
- Sequential Data
- Combining Models
Appendix
- Data Sets
- Probability Distributions
- Properties of Matrices
- Calculus of Variations
- Lagrange Multipliers
Pattern Recognition and Machine Learning.
Christopher M. Bishop.
www.microsoft.com .
Feb 1, 2006.