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Introduction
to
Machine
Learning
Second
Edition

Adaptive Computation and Machine Learning

Thomas Dietterich, Editor
Christopher Bishop, David Heckerman, Michael Jordan, and Michael
Kearns, Associate Editors

A complete list of books published in The Adaptive Computation and
Machine Learning series appears at the back of this book.

Introduction
to
Machine
Learning

Second
Edition

Ethem Alpaydın

The MIT Press
Cambridge, Massachusetts
London, England

© 2010 Massachusetts Institute of Technology
All rights reserved. No part of this book may be reproduced in any form by any
electronic or mechanical means (including photocopying, recording, or information storage and retrieval) without permission in writing from the publisher.
For information about special quantity discounts, please email
special_sales@mitpress.mit.edu.
Typeset in 10/13 Lucida Bright by the author using LATEX 2ε .
Printed and bound in the United States of America.
Library of Congress Cataloging-in-Publication Information
Alpaydin, Ethem.
Introduction to machine learning / Ethem Alpaydin. — 2nd ed.
p. cm.
Includes bibliographical references and index.
ISBN 978-0-262-01243-0 (hardcover : alk. paper)
1. Machine learning. I. Title
Q325.5.A46 2010
006.3’1—dc22
2009013169
CIP

10 9 8 7 6 5 4 3 2 1

Brief Contents

1

Introduction

2

Supervised Learning

1

3

Bayesian Decision Theory

4

Parametric Methods

5

Multivariate Methods

6

Dimensionality Reduction

7

Clustering

8

Nonparametric Methods

9

Decision Trees

21
47

61
87
109

143
163

185

10 Linear Discrimination

209

11 Multilayer Perceptrons
12 Local Models

233

279

13 Kernel Machines

309

14 Bayesian Estimation

341

15 Hidden Markov Models
16 Graphical Models

363

387

17 Combining Multiple Learners
18 Reinforcement Learning

419

447

19 Design and Analysis of Machine Learning Experiments
A Probability

517

475

Contents

Series Foreword
Figures

xix

Tables

xxix

Preface

xvii

xxxi

Acknowledgments

xxxiii

Notes for the Second Edition
Notations

xxxix

1 Introduction
1.1
1.2

1.3
1.4
1.5
1.6

1

What Is Machine Learning?
1
Examples of Machine Learning Applications
1.2.1 Learning Associations
4
1.2.2 Classification
5
1.2.3 Regression
9
1.2.4 Unsupervised Learning
11
1.2.5 Reinforcement Learning
13
Notes
14
Relevant Resources
16
Exercises
18
References
19

2 Supervised Learning
2.1

xxxv

21

Learning a Class from Examples

21

4

viii

Contents

2.2
2.3
2.4
2.5
2.6
2.7
2.8
2.9
2.10
2.11

Vapnik-Chervonenkis (VC) ...
Introduction
to
Machine
Learning
Second
Edition
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