An Introduction to Pattern Recognition and Machine Learning - Table of Contents

The chapters of the text are intended to be read in sequence, although the case studies and examples are designed to be relatively stand-alone and accessible to most readers.

The text aims to be mathematically clear and rigorous, but emphasizing high-level concepts and effects.

Chapter 1: Overview

Chapter 2: Introduction to Pattern Recognition

Chapter 3: Learning

Chapter 4: Representing Patterns

Chapter 5: Feature Extraction and Selection

Chapter 6: Distance-Based Classification

Chapter 7: Inferring Class Models

Chapter 8: Statistics-Based Classification

Chapter 9: Classifier Testing and Validation

Chapter 10: Discriminant-Based Classification

Chapter 11: Ensemble Classification

Chapter 12: Model-Free Classification

Appendix A: Algebra Review

Appendix B: Random Variables and Random Vectors

Appendix C: Introduction to Optimization

Appendix D: Mathematical Derivations