Machine Learning, revised and updated edition
Reduzierte Artikel in dieser Kategorie
| Verlag | MIT Press |
| Auflage | 2021 |
| Seiten | 280 |
| Format | 12,8 x 17,7 x 1,4 cm |
| Gewicht | 256 g |
| Artikeltyp | Englisches Buch |
| Reihe | The MIT Press Essential Knowledge series |
| EAN | 9780262542524 |
| Bestell-Nr | 26254252EA |
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.
Inhaltsverzeichnis:
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