This book is an introductory text on machine learning. The style of the book is such that it can be used as a textbook for an advanced undergraduate or graduate course, at the same time aiming at interested academics and professionals with a background in neighbouring disciplines. The material includes necessary mathematical detail, but emphasises intuitions and how-to.
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 here, or see the Table of Contents below.
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