
Foundations of Machine Learning
MIT Press
Published on 17. August 2012
Book
Hardback
432 pages
978-0-262-01825-8 (ISBN)
Description
Fundamental topics in machine learning are presented along with theoretical and conceptual tools for the discussion and proof of algorithms.This graduate-level textbook introduces fundamental concepts and methods in machine learning. It describes several important modern algorithms, provides the theoretical underpinnings of these algorithms, and illustrates key aspects for their application. The authors aim to present novel theoretical tools and concepts while giving concise proofs even for relatively advanced topics.Foundations of Machine Learning fills the need for a general textbook that also offers theoretical details and an emphasis on proofs. Certain topics that are often treated with insufficient attention are discussed in more detail here; for example, entire chapters are devoted to regression, multi-class classification, and ranking. The first three chapters lay the theoretical foundation for what follows, but each remaining chapter is mostly self-contained. The appendix offers a concise probability review, a short introduction to convex optimization, tools for concentration bounds, and several basic properties of matrices and norms used in the book.The book is intended for graduate students and researchers in machine learning, statistics, and related areas; it can be used either as a textbook or as a reference text for a research seminar.
More details
Series
Language
English
Place of publication
Cambridge, Mass.
United States
Publishing group
MIT Press Ltd
Target group
College/higher education
Interest Age: From 18 years
Product notice
Cloth over boards
Illustrations
40 s/w Abbildungen, 55 farbige Abbildungen
55 color illus., 40 b&w illus.
Dimensions
Height: 229 mm
Width: 178 mm
Thickness: 27 mm
Weight
1111 gr
ISBN-13
978-0-262-01825-8 (9780262018258)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Persons
Mehryar Mohri is Professor of Computer Science at New York University's Courant Institute of Mathematical Sciences and a Research Consultant at Google Research.
Afshin Rostamizadeh is a Research Scientist at Google Research.
Ameet Talwalkar is a National Science Foundation Postdoctoral Fellow in the Department of Electrical Engineering and Computer Science at the University of California, Berkeley.
Afshin Rostamizadeh is a Research Scientist at Google Research.
Ameet Talwalkar is a National Science Foundation Postdoctoral Fellow in the Department of Electrical Engineering and Computer Science at the University of California, Berkeley.
Author
New York University
Google, Inc.
University of California, Berkeley