A Concise Introduction to Machine Learning
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 1. Introduction
Chapter 2. Probability Theory
Chapter 3. Sampling
Chapter 4. Linear Classification
Chapter 5. Non-Linear Classification
Chapter 6. Dimensionality Reduction
Chapter 7. Regression
Chapter 8. Feature Learning
"This book aims to present a concise yet rigorous introduction to several basic topics in machine
learning. The concepts and algorithms are comprehensively explained with intuition and illustrative examples in MATLAB, using mathematics as the common language. The focus is on
why and how an algorithm works...this book covers the mathematical foundation, the techniques and applications in machine learning well. It may be useful for readers with some background in mathematics who wish to extend themselves in statistics and machine learning, such as statisticians, graduate and senior undergraduate students."
-- Shuangzhe Liu, Professor, University of Canberra
"Data scientist Faul (British Antarctic Survey) aspires to provide the much-needed reference for identifying appropriate machine learning (ML) algorithms suitable for problem solving scenarios. Because ML concepts will vary in name across disciplines, she adopts mathematics as a common language to offset such differences. The text emphasizes why and how ML algorithms are successful and identifies the type of problem best addressed by each algorithm covered, as well as the commonalities shared between various algorithms... For readers who already have some experience in ML or a mature understanding of probability and statistics, this text indeed offers a worthwhile reference."
--M. Mounts, Dartmouth College