Understanding Machine Learning: From Theory to Algorithms - Hardcover

Shalev-Shwartz; Ben-David

Hardcover

ISBN 10: 1107057132 ISBN 13: 9781107057135

Verlag: Cambridge University Press, 2014

Inhaltsangabe

Machine learning is one of the fastest growing areas of computer science, with far-reaching applications. This book explains the principles behind the automated learning approach and the considerations underlying its usage. The authors explain the 'hows' and 'whys' of machine-learning algorithms, making the field accessible to both students and practitioners.

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Críticas

'This elegant book covers both rigorous theory and practical methods of machine learning. This makes it a rather unique resource, ideal for all those who want to understand how to find structure in data.' Bernhard Schölkopf, Max Planck Institute for Intelligent Systems, Germany

'This is a timely text on the mathematical foundations of machine learning, providing a treatment that is both deep and broad, not only rigorous but also with intuition and insight. It presents a wide range of classic, fundamental algorithmic and analysis techniques as well as cutting-edge research directions. This is a great book for anyone interested in the mathematical and computational underpinnings of this important and fascinating field.' Avrim Blum, Carnegie Mellon University

'This text gives a clear and broadly accessible view of the most important ideas in the area of full information decision problems. Written by two key contributors to the theoretical foundations in this area, it covers the range from theoretical foundations to algorithms, at a level appropriate for an advanced undergraduate course.' Peter L. Bartlett, University of California, Berkeley

Biografía del autor

Shai Shalev-Shwartz is an Associate Professor at the School of Computer Science and Engineering at the Hebrew University of Jerusalem, Israel.

Shai Ben-David is a Professor in the School of Computer Science at the University of Waterloo, Canada.

Über diese Auflage

• Verlag: Cambridge University Press
• Erscheinungsdatum: 2014
• ISBN 10: 1107057132
• ISBN 13: 9781107057135
• Einband: Tapa dura
• Anzahl der Seiten: 409