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

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9780262018258: Foundations of Machine Learning (Adaptive Computation and Machine Learning series)

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.

Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.

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.

Review :

A solid, comprehensive, and self-contained book providing a uniform treatment of a very broad collection of machine learning algorithms and problems. Foundations of Machine Learning is an essential reference book for corporate and academic researchers, engineers, and students.

(Corinna Cortes, Head of Google Research, NY)

Finally, a book that is both broad enough to cover many algorithmic topics of machine learning and mathematically deep enough to introduce the required theory for a graduate level course. Foundations of Machine Learning is a great achievement and a significant contribution to the machine learning community.

(Yishay Mansour, School of Computer Science, Tel Aviv University)

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 comments provided 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)

Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.

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Mehryar Mohri; Afshin Rostamizadeh; Ameet Talwalkar
ISBN 10 : 026201825X ISBN 13 : 9780262018258
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Mehryar Mohri, Afshin Rostamizadeh, Ameet Talwalkar
Edité par MIT Press 2012-09-07, Cambridge, MA (2012)
ISBN 10 : 026201825X ISBN 13 : 9780262018258
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Description du livre MIT Press 2012-09-07, Cambridge, MA, 2012. hardback. État : New. N° de réf. du libraire 9780262018258

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Mohri, Mehryar; Rostamizadeh, Afshin; Talwalkar, Ameet
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Description du livre MIT Press Ltd, 2012. État : New. 2012. Hardcover. Series: Adaptive Computation and Machine Learning Series. Num Pages: 432 pages, 55 color illus., 40 b&w illus. BIC Classification: UYQM. Category: (G) General (US: Trade). Dimension: 235 x 178 x 28. Weight in Grams: 1104. . . . . . . N° de réf. du libraire V9780262018258

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Description du livre MIT Press 2012-09-07, 2012. Hardcover. État : New. N° de réf. du libraire NU-GRD-04896955

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Description du livre MIT Press Ltd. État : New. 2012. Hardcover. Series: Adaptive Computation and Machine Learning Series. Num Pages: 432 pages, 55 color illus., 40 b&w illus. BIC Classification: UYQM. Category: (G) General (US: Trade). Dimension: 235 x 178 x 28. Weight in Grams: 1104. . . . . . Books ship from the US and Ireland. N° de réf. du libraire V9780262018258

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Mehryar Mohri; Afshin Rostamizadeh; Ameet Talwalkar
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Description du livre The MIT Press, 2012. Hardcover. État : New. book. N° de réf. du libraire 026201825X

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Description du livre MIT Press Ltd, United States, 2012. Hardback. État : New. Language: English . Brand New Book. 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. N° de réf. du libraire AAU9780262018258

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Mehryar Mohri, Afshin Rostamizadeh, Ameet Talwalkar
Edité par MIT Press Ltd, United States (2012)
ISBN 10 : 026201825X ISBN 13 : 9780262018258
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Description du livre MIT Press Ltd, United States, 2012. Hardback. État : New. Language: English . Brand New Book. 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. N° de réf. du libraire AAU9780262018258

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Mehryar Mohri, Afshin Rostamizadeh, Ameet Talwalkar
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ISBN 10 : 026201825X ISBN 13 : 9780262018258
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Description du livre MIT Press Ltd. Hardback. État : new. BRAND NEW, Foundations of Machine Learning, Mehryar Mohri, Afshin Rostamizadeh, Ameet Talwalkar, 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. N° de réf. du libraire B9780262018258

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