The emphasis of the book is on the question of Why – only if why an algorithm is successful is understood, can it be properly applied, and the results trusted. Algorithms are often taught side by side without showing the similarities and differences between them. This book addresses the commonalities, and aims to give a thorough and in-depth treatment and develop intuition, while remaining concise.

This useful reference should be an essential on the bookshelves of anyone employing machine learning techniques.

The author's webpage for the book can be accessed here.

chapter Chapter 1|6 pages


chapter Chapter 2|55 pages

Probability Theory

chapter Chapter 3|25 pages


chapter Chapter 4|19 pages

Linear Classification

chapter Chapter 5|40 pages

Non-Linear Classification

chapter Chapter 6|40 pages


chapter Chapter 7|23 pages

Dimensionality Reduction

chapter Chapter 8|49 pages


chapter Chapter 9|34 pages

Feature Learning