Click here for questions on the book.

The challenge in writing an introductory machine learning text is to do justice to the incredible richness of the machine learning field without losing sight of the unifying principles.

One way in which this is achieved in this book is by separate and extensive treatment of tasks and features, both of which are common across any machine learning approaches. Covering a wide range of logical, geometric and statistical models, the book is one of the most comprehensive machine learning texts around.

For excerpts and lecture slides click

- Prologue: A machine learning sampler
- 1. The ingredients of machine learning
- 2. Binary classification and related tasks
- 3. Beyond binary classification
- 4. Concept learning
- 5. Tree models
- 6. Rule models
- 7. Linear models
- 8. Distance-based models
- 9. Probabilistic models
- 10. Features
- 11. Model ensembles
- 12. Machine learning experiments
- • Epilogue: Where to go from here
- • Important points to remember
- • Bibliography
- • Index

- Excellent introductory text with significant depth for professionals: future classic...
- The author does an *exceptionally* good job using graphics to build intuitions about what algoritare used for and how they work...
- What an amazing book, I got it about a month ago for a self-study routine and every page of this has been a joy...
- When it comes to Machine Learning, it is usually difficult for me to really understand the science on a deep level. This book changed that for me...
- One bright spot in the otherwise pitch black landscape of machine learning books...

- (December 2013)
__Full set of lecture slides in PDF and LaTeX beamer__ __Click here to sign up for updates & receive the first chapter free.__

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