Understanding Machine Learning: From Theory to Algorithms

Shalev-shwartz, Shai/ Ben-David, Shai

ISBN 10: 1107057132 / ISBN 13: 9781107057135

Edité par Cambridge Univ Pr, 2014

A propos de cet article

397 pages. 10.00x7.00x1.00 inches. In Stock. N° de réf. du vendeur x-1107057132

Détails bibliographiques

Titre : Understanding Machine Learning: From Theory ...

Éditeur : Cambridge Univ Pr

Date d'édition : 2014

Reliure : Hardcover

Etat : Brand New

A propos de ce titre

Revue de presse:

'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