Foundations of Machine Learning

Foundations of Machine Learning

Hardback Adaptive Computation and Machine Learning

By (author) Mehryar Mohri, By (author) Afshin Rostamizadeh, By (author) Ameet Talwalkar

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  • Publisher: MIT Press
  • Format: Hardback | 432 pages
  • Dimensions: 178mm x 229mm x 28mm | 1,111g
  • Publication date: 7 September 2012
  • Publication City/Country: Cambridge, Mass.
  • ISBN 10: 026201825X
  • ISBN 13: 9780262018258
  • Illustrations note: 55 color illus., 40 b&w illus.
  • Sales rank: 300,388

Product description

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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Author information

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.

Review quote

"In my opinion, the content of the book is outstanding in terms of clarity of discourse and the variety of well-selected examples and exercises. The enlightening commentsprovided by the author at the end of each chapter and the suggestions for further reading are also important features of the book. The concepts and methods are presented in a very clear and accessible way and the illustrative examples contribute substantially to facilitating the understanding of the overall work." -- Computing Reviews