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Foundations of Machine Learning (Adaptive Computation and Machine Learning)

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Foundations of Machine Learning (Adaptive Computation and Machine Learning) Cover

 

Synopses & Reviews

Publisher Comments:

andlt;Pandgt;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. andlt;Iandgt;Foundations of Machine Learningandlt;/Iandgt; 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.andlt;/Pandgt;andlt;Pandgt;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. andlt;/Pandgt;

Synopsis:

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.

About the Author

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.

Product Details

ISBN:
9780262018258
Author:
Mohri, Mehryar
Publisher:
The MIT Press
Author:
Rostamizadeh, Afshin
Author:
Massachusetts Institute of Technology
Author:
Talwalkar, Ameet
Location:
Cambridge
Subject:
Machine Theory
Subject:
Computers-Reference - General
Copyright:
Series:
Adaptive Computation and Machine Learning series Foundations of Machine Learning
Publication Date:
20120817
Binding:
Hardback
Language:
English
Illustrations:
55 color illus., 40 b, &, w illus.
Pages:
432
Dimensions:
9 x 7 in
Age Level:
from 18

Related Subjects

» Computers and Internet » Computers Reference » General
» Science and Mathematics » Environmental Studies » General

Foundations of Machine Learning (Adaptive Computation and Machine Learning) New Hardcover
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$85.95 In Stock
Product details 432 pages MIT Press (MA) - English 9780262018258 Reviews:
"Synopsis" by , 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.

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