Understanding Machine Learning: From Theory to Algorithms

Shai Shalev-Shwartz

ISBN 10: 1107057132 / ISBN 13: 9781107057135

Verlag: Cambridge University Press, 2014

Beschreibung

Introduces machine learning and its algorithmic paradigms, explaining the principles behind automated learning approaches and the considerations underlying their usage. Num Pages: 409 pages, 47 b/w illus. 123 exercises. BIC Classification: UYQM. Category: (U) Tertiary Education (US: College). Dimension: 186 x 261 x 29. Weight in Grams: 912. . 2014. 1st Edition. Hardcover. . . . . Bestandsnummer des Verkäufers V9781107057135

Bibliografische Details

Titel: Understanding Machine Learning: From Theory ...

Verlag: Cambridge University Press

Erscheinungsdatum: 2014

Einband: Hardcover

Zustand: New

Auflage: 1. Auflage

Über diesen Titel

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.

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