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Summary

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.

Hardcover Out of Print ISBN: 9780262018258 432 pp. | 7 in x 9 in 55 color illus., 40 b&w illus. August 2012

Authors Mehryar Mohri 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 Afshin Rostamizadeh is a Research Scientist at Google Research. Ameet Talwalkar Ameet Talwalkar is Assistant Professor in the Machine Learning Department at Carnegie Mellon University.

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Foundations of Machine Learning | The MIT Press