Former academic library book, with library bookplate to front pastedown; ink stamps to endpapers and text-block edges, including withdrawn stamp; spine label and shelf number to spine. Some pages a little marked and creased, but free from notes or highlighting. Overall, sound and serviceable. Publisher's note: "This textbook offers a comprehensive and self-contained introduction to the field of machine learning, based on a unified, probabilistic approach. The coverage combines breadth and depth, offering necessary background material on such topics as probability, optimization, and linear algebra as well as discussion of recent developments in the field, including conditional random fields, L1 regularization, and deep learning. The book is written in an informal, accessible style, complete with pseudo-code for the most important algorithms. All topics are copiously illustrated with color images and worked examples drawn from such application domains as biology, text processing, computer vision, and robotics. Rather than providing a cookbook of different heuristic methods, the book stresses a principled model-based approach, often using the language of graphical models to specify models in a concise and intuitive way. Almost all the models described have been implemented in a MATLAB software package--PMTK (probabilistic modeling toolkit)--that is freely available online"--Back cover. Size: 23.6 x 21.1 x 4.1 cm. xxix, 1067 pp. Shipped Weight: 1-2 kilos. Category: Computers & Internet; Machine learning; Probabilities; Probabilities; ISBN: 0262018020. ISBN/EAN: 9780262018029. Add. Inventory No: 250310SH7205.

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Detalles bibliogr�ficos

T�tulo

Machine Learning : A Probabilistic Perspective

Editor

The MIT Press, Cambridge, Massachusetts

Fecha de publicaci�n

2012

Idioma

Ingl�s

ISBN 10

0262018020

ISBN 13

9780262018029

Encuadernaci�n

Hardcover

Estado

Good

Cat�logos de vendedores

Computers & Internet

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Sinopsis

A comprehensive introduction to machine learning that uses probabilistic models and inference as a unifying approach.

Today's Web-enabled deluge of electronic data calls for automated methods of data analysis. Machine learning provides these, developing methods that can automatically detect patterns in data and then use the uncovered patterns to predict future data. This textbook offers a comprehensive and self-contained introduction to the field of machine learning, based on a unified, probabilistic approach.

The coverage combines breadth and depth, offering necessary background material on such topics as probability, optimization, and linear algebra as well as discussion of recent developments in the field, including conditional random fields, L1 regularization, and deep learning. The book is written in an informal, accessible style, complete with pseudo-code for the most important algorithms. All topics are copiously illustrated with color images and worked examples drawn from such application domains as biology, text processing, computer vision, and robotics. Rather than providing a cookbook of different heuristic methods, the book stresses a principled model-based approach, often using the language of graphical models to specify models in a concise and intuitive way. Almost all the models described have been implemented in a MATLAB software package—PMTK (probabilistic modeling toolkit)—that is freely available online. The book is suitable for upper-level undergraduates with an introductory-level college math background and beginning graduate students.

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Kevin P. Murphy is a Senior Staff Research Scientist at Google Research.

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I'm an online bookseller based in Oxford, UK, with a varied stock, and a particular interest in scholarly non-fiction.

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