Machine.Learning.The.New.AI.pdf

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CONTENTS

SERIES FOREWORD

PREFACE

1 WHY WE ARE INTERESTED IN MACHINE LEARNING

The Power of the Digital

Computers Store Data

Computers Exchange Data

Mobile Computing

Social Data

All That Data: The Dataquake

Learning versus Programming

Artificial Intelligence

Understanding the Brain

Pattern Recognition

What We Talk about When We Talk about Learning

History

2 MACHINE LEARNING, STATISTICS, AND DATA ANALYTICS

Learning to Estimate the Price of a Used Car

Randomness and Probability

Learning a General Model

Model Selection

Supervised Learning

Learning a Sequence

Credit Scoring

Expert Systems

Expected Values

3 PATTERN RECOGNITION

Learning to Read

Matching Model Granularity

Generative Models

Face Recognition

Speech Recognition

Natural Language Processing and Translation

Combining Multiple Models

Outlier Detection

Dimensionality Reduction

Decision Trees

Active Learning

Learning to Rank

Bayesian Methods

4 NEURAL NETWORKS AND DEEP LEARNING

Artificial Neural Networks

Neural Network Learning Algorithms

What a Perceptron Can and Cannot Do

Connectionist Models in Cognitive Science

Neural Networks as a Paradigm for Parallel Processing

Hierarchical Representations in Multiple Layers

Deep Learning

5 LEARNING CLUSTERS AND RECOMMENDATIONS

Finding Groups in Data

Recommendation Systems

6 LEARNING TO TAKE ACTIONS

Reinforcement Learning

Armed Bandit

Temporal Difference Learning

Reinforcement Learning Applications

7 WHERE DO WE GO FROM HERE?

Make Them Smart, Make Them Learn

High-Performance Computation

Data Mining

Data Privacy and Security

Data Science

Machine Learning, Artificial Intelligence, and the Future

Closing Remarks

NOTES

GLOSSARY

REFERENCES

FURTHER READINGS

INDEX

ETHEM ALPAYDIN

MACHINE LEARNING 010001010111010001101000 011001010110110100100000 010000010110110001110000 011000010111100101100100 011010010110111000001101 000010100100110101100001 011000110110100001101001 011011100110010100100000 010011000110010101100001 011100100110111001101001 011011100110011100001101 000010100101010001101000 011001010010000001001110 011001010111011100100000 010000010100100100001101 00001010

The MIT Press Essential Knowledge Series Auctions, Timothy P. Hubbard and Harry J. Paarsch Cloud Computing, Nayan Ruparelia Computing: A Concise History, Paul E. Ceruzzi The Conscious Mind, Zoltan L. Torey Crowdsourcing, Daren C. Brabham Free Will, Mark Balaguer Information and Society, Michael Buckland Information and the Modern Corporation, James W. Cortada Intellectual Property Strategy, John Palfrey The Internet of Things, Samuel Greengard Machine Learning: The New AI, Ethem Alpaydın Memes in Digital Culture, Limor Shifman Metadata, Jeffrey Pomerantz The Mind–Body Problem, Jonathan Westphal MOOCs, Jonathan Haber Neuroplasticity, Moheb Costandi Open Access, Peter Suber Paradox, Margaret Cuonzo Robots, John Jordan Self-Tracking, Gina Neff and Dawn Nafus Sustainability, Kent E. Portney The Technological Singularity, Murray Shanahan Understanding Beliefs, Nils J. Nilsson Waves, Frederic Raichlen

MACHINE LEARNING THE NEW AI ETHEM ALPAYDIN The MIT Press Cambridge, Massachusetts London, England

© 2016 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. This book was set in Chaparral and DIN by Toppan Best-set Premedia Limited. Printed and bound in the United States of America. Library of Congress Cataloging-in-Publication Data Names: Alpaydın, Ethem, author. Title: Machine learning : the new AI / Ethem Alpaydın. Description: Cambridge, MA : MIT Press, [2016] | Series: MIT Press essential knowledge series | Includes bibliographical references and index. Identifiers: LCCN 2016012342 | ISBN 9780262529518 (pbk. : alk. paper) Subjects: LCSH: Machine learning. | Artificial intelligence. Classification: LCC Q325.5 .A47 2016 | DDC 006.3/1—dc23 LC record available at https://lccn.loc.gov/2016012342 10 9 8 7 6 5 4 3 2 1

CONTENTS Series Foreword Preface ix vii 1 Why We Are Interested in Machine Learning 1 2 Machine Learning, Statistics, and Data Analytics 29 3 Pattern Recognition 55 4 Neural Networks and Deep Learning 85 5 Learning Clusters and Recommendations 111 6 Learning to Take Actions 125 7 Where Do We Go from Here? 141 Notes 169 Glossary 171 References 183 Further Readings 187 Index 189

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