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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.
About the Author: Kevin P. Murphy is a Research Scientist at Google. Previously, he was Associate Professor of Computer Science and Statistics at the University of British Columbia.
Title: Machine Learning A Probabilistic Perspective
Publisher: MIT Press
Publication Date: 2012
Binding: Hardcover
Condition: Good
Seller: HPB-Red, Dallas, TX, U.S.A.
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Seller: Harry Righton, Evesham, United Kingdom
Hardcover. Condition: Very Good. No Jacket. no dustjacket. illus covers. 1071 pages. Size: 8vo - over 7¾ - 9¾" tall. Book. Seller Inventory # 910192
Seller: Anybook.com, Lincoln, United Kingdom
Condition: Fair. This is an ex-library book and may have the usual library/used-book markings inside.This book has hardback covers. In fair condition, suitable as a study copy. No dust jacket. Please note the Image in this listing is a stock photo and may not match the covers of the actual item,2050grams, ISBN:9780262018029. Seller Inventory # 9547682
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Seller: Anybook.com, Lincoln, United Kingdom
Condition: Fair. This is an ex-library book and may have the usual library/used-book markings inside.This book has hardback covers. In fair condition, suitable as a study copy. No dust jacket. Please note the Image in this listing is a stock photo and may not match the covers of the actual item,2050grams, ISBN:9780262018029. Seller Inventory # 9547683
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hardcover. Condition: Good. Illustrated. Ships in a BOX from Central Missouri! May not include working access code. Will not include dust jacket. Has used sticker(s) and some writing or highlighting. UPS shipping for most packages, (Priority Mail for AK/HI/APO/PO Boxes). Seller Inventory # 001320430U
Seller: Barnaby, Oxford, United Kingdom
Hardcover. Condition: Good. 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. Seller Inventory # 250310SH7205
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Seller: Basi6 International, Irving, TX, U.S.A.
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