Pattern Recognition and Machine Learning

This is the first textbook on pattern recognition to present the Bayesian viewpoint. The book presents approximate inference algorithms that permit fast approximate answers in situations where exact answers are not feasible. It uses graphical models to describe probability distributions when no other books apply graphical models to machine learning. No previous knowledge of pattern recognition or machine learning concepts is assumed. Familiarity with multivariate calculus and basic linear algebra is required, and some experience in the use of probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory.

Table of Contents

Chapter 1. Introduction
Chapter 2. Probability Distributions
Chapter 3. Linear Models for Regression
Chapter 4. Linear Models for Classification
Chapter 5. Neural Networks
Chapter 6. Kernel Methods
Chapter 7. Sparse Kernel Machines
Chapter 8. Graphical Models
Chapter 9. Mixture Models and EM
Chapter 10. Approximate Inference
Chapter 11. Sampling Methods
Chapter 12. Continuous Latent Variables
Chapter 13. Sequential Data
Chapter 14. Combining Models

Appendix A. Data Sets
Appendix B. Probability Distributions
Appendix C. Properties of Matrices
Appendix D. Calculus of Variations
Appendix E. Lagrange Multipliers