**Hardcover**|

**$63.00 Text**|

**£43.95**| ISBN: 9780262028189 | 640 pp. | 8 x 9 in | 192 b&w illus.| August 2014

**eBook**|

**$63.00 Text**| ISBN: 9780262325738 | 640 pp. | 192 b&w illus.| August 2014

About MIT Press eBooks

## Look Inside

## Instructor Resources

## Introduction to Machine Learning, Third Edition

## Overview

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.

## Instructor Resources for This Title:

## About the Author

Ethem Alpaydin is a Professor in the Department of Computer Engineering at Bogaziçi University, Istanbul.

## Endorsements

*Introduction to Machine Learning*provides a nice blending of the topical coverage of machine learning (à la Tom Mitchell) with formal probabilistic foundations (à la Christopher Bishop). This newly updated version now introduces some of the most recent and important topics in machine learning (e.g., spectral methods, deep learning, and learning to rank) to students and researchers of this critically important and expanding field.”

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**John W. Sheppard**, Professor of Computer Science, Montana State University

*Introduction to Machine Learning*for several years in my graduate Machine Learning course. The book provides an ideal balance of theory and practice, and with this third edition, extends coverage to many new state-of-the-art algorithms. I look forward to using this edition in my next Machine Learning course.”

—

**Larry Holder**, Professor of Electrical Engineering and Computer Science, Washington State University

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**Hilario Gómez-Moreno**, IEEE Senior Member, University of Alcalá, Spain