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