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“The coverage is very unique and I like the way that the theory is interspersed with applications and python scripts. I don’t know any other book that covers ML in such an integrated manner.” — Alfred Hero, Professor, University of Michigan, USA “I think the selection of topics is really nice. Also, the math is very clearly written; I’m sure it will be greatly appreciated.” — Gábor Lugosi, Research Professor, Pompeu-Fabra University, Spain SpringerLink | Google Books | Amazon | Barnes and Noble

Book front matter
Book back matter (including all appendices)
Book errata
Data sets used in the book

Chapter 1 Introduction: Lecture slides | Figures | Python scripts

Chapter 2 Optimal Classification: Lecture slides | Figures | Python script

Chapter 3 Sample-Based Classification: Lecture slides | Figures

Chapter 4 Parametric Classification: Lecture slides | Figures

Chapter 5 Nonparametric Classification: Lecture slides | Figures | Python scripts

Chapter 6 Function-Approximation Classification: Lecture slides | Figures | Python scripts

Chapter 7 Error Estimation for Classification: Lecture slides | Figures | Python scripts

Chapter 8 Model Selection for Classification: Lecture slides | Figures

Chapter 9 Dimensionality Reduction: Lecture slides | Figures | Python scripts

Chapter 10 Clustering: Lecture slides | Figures | Python scripts

Chapter 11 Regression: Lecture slides | Figures | Python scripts

Appendix: Figures | Python script

Note: The lecture slides do not include material from the starred or additional topics sections. Small additions were made to the neural network section in the lecture slides for chapter 6.

An instructor’s manual with problem solutions can be obtained by contacting the publisher or e-mailing the author using an institutional e-mail address.
Please send corrections, comments, and suggestions to ulisses@ece.tamu.edu