Summary

A substantially revised third edition of a comprehensive textbook that covers a broad range of topics not often included in introductory texts.

The goal of machine learning is to program computers to use example data or past experience to solve a given problem. Many successful applications of machine learning exist already, including systems that analyze past sales data to predict customer behavior, optimize robot behavior so that a task can be completed using minimum resources, and extract knowledge from bioinformatics data. Introduction to Machine Learnin g is a comprehensive textbook on the subject, covering a broad array of topics not usually included in introductory machine learning texts. Subjects include supervised learning; Bayesian decision theory; parametric, semi-parametric, and nonparametric methods; multivariate analysis; hidden Markov models; reinforcement learning; kernel machines; graphical models; Bayesian estimation; and statistical testing.

Machine learning is rapidly becoming a skill that computer science students must master before graduation. The third edition of Introduction to Machine Learning reflects this shift, with added support for beginners, including selected solutions for exercises and additional example data sets (with code available online). Other substantial changes include discussions of outlier detection; ranking algorithms for perceptrons and support vector machines; matrix decomposition and spectral methods; distance estimation; new kernel algorithms; deep learning in multilayered perceptrons; and the nonparametric approach to Bayesian methods. All learning algorithms are explained so that students can easily move from the equations in the book to a computer program. The book can be used by both advanced undergraduates and graduate students. It will also be of interest to professionals who are concerned with the application of machine learning methods.

Choice Review

This work, an excellent addition to the literature in the growing field of machine learning, or more generally, artificial intelligence, is geared to graduate students with good backgrounds in mathematics and computer science algorithms as well as statistics. Alpaydin (Bog~zici Univ., Turkey) takes a very theoretical approach; thus, readers should be prepared to work through the mathematical formulas and implement the algorithms on their own, which is the most effective way to comprehend the material. Students who learn from this book will then have a solid foundation to explore more in-depth studies of neural networks, supervised and unsupervised learning, decision trees, and other areas of this complex field. References at the end of the book, many related to statistics, will help students further extend their knowledge of the topics presented. This new edition (1st ed., 2004) was completely revised and includes several new chapters and exercises. It is a valuable contribution to a discipline defined by such important researchers as Nils Nilsson, John McCarty, Marvin Minsky, Tom Mitchell, Stuart Russell, and Peter Norwig. Summing Up: Highly recommended. Graduate students and above. J. Brzezinski formerly, DePaul University