UNDERSTANDING MACHINE LEARNING: FROM THEORY TO ALGORITHMS

Shai Shalev-Shwartz

ISBN 10: 1107057132 / ISBN 13: 9781107057135

Edité par Cambridge University Press, 2014

New.SoftCover International edition. Different ISBN and Cover image but contents are same as US edition. Expediting shipping for all USA and Europe orders excluding PO Box. Excellent Customer Service. N° de réf. du vendeur ABEJUNE24-150481

Synopsis :

Machine learning is one of the fastest growing areas of computer science, with far-reaching applications. The aim of this textbook is to introduce machine learning, and the algorithmic paradigms it offers, in a principled way. The book provides a theoretical account of the fundamentals underlying machine learning and the mathematical derivations that transform these principles into practical algorithms. Following a presentation of the basics, the book covers a wide array of central topics unaddressed by previous textbooks. These include a discussion of the computational complexity of learning and the concepts of convexity and stability; important algorithmic paradigms including stochastic gradient descent, neural networks, and structured output learning; and emerging theoretical concepts such as the PAC-Bayes approach and compression-based bounds. Designed for advanced undergraduates or beginning graduates, the text makes the fundamentals and algorithms of machine learning accessible to students and non-expert readers in statistics, computer science, mathematics and engineering.

À propos des auteurs: 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.

Détails bibliographiques

Titre : UNDERSTANDING MACHINE LEARNING: FROM THEORY ...
Éditeur : Cambridge University Press
Date d'édition : 2014
Reliure : Couverture souple
Etat : Brand New
Edition : Edition internationale