Machine Learning, revised and updated edition

A concise overview of machine learning--computer programs that learn from data--the basis of such applications as voice recognition and driverless cars.

Today, machine learning underlies a range of applications we use every day, from product recommendations to voice recognition--as well as some we don't yet use everyday, including driverless cars. It is the basis for a new approach to artificial intelligence that aims to program computers to use example data or past experience to solve a given problem. In this volume in the MIT Press Essential Knowledge series, Ethem Alpaydin offers a concise and accessible overview of "the new AI." This expanded edition offers new material on such challenges facing machine learning as privacy, security, accountability, and bias.
 
Alpaydin, author of a popular textbook on machine learning, explains that as "Big Data" has gotten bigger, the theory of machine learning--the foundation of efforts to process that data into knowledge--has also advanced. He describes the evolution of the field, explains important learning algorithms, and presents example applications. He discusses the use of machine learning algorithms for pattern recognition; artificial neural networks inspired by the human brain; algorithms that learn associations between instances; and reinforcement learning, when an autonomous agent learns to take actions to maximize reward. In a new chapter, he considers transparency, explainability, and fairness, and the ethical and legal implications of making decisions based on data.

Ethem Alpaydín is Professor in the Department of Computer Engineering at Özyegin University and a member of the Science Academy, Istanbul. He is the author of the widely used textbook, Introduction to Machine Learning (MIT Press), now in its fourth edition.

Series Foreword vii
Preface ix
1 Why We Are Interested in Machine Learning 1
2 Machine Learning, Statistics, and Data Analytics 35
3 Pattern Recognition 71
4 Neural Networks and Deep Learning 105
5 Learning Clusters and Recommendations 143
6 Learning to Take Action 159
7 Challenges and Risks 183
8 Where Do We Go from Here? 201
Glossary 227
Notes 239
References 243
Further Reading 247
Index 249