Machine Learning: A Probabilistic Perspective (Adaptive Computation and Machine Learning series)
Murphy, Kevin P.
ISBN 10: 0262018020 ISBN 13: 9780262018029
Published by The MIT Press, 2012
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hardcover
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About this Item
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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
Synopsis:
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...
About the Author: Kevin P. Murphy is a Senior Staff Research Scientist at Google Research.
"About this title" may belong to another edition of this title.
Bibliographic Details
Title: Machine Learning: A Probabilistic ...
Publisher: The MIT Press
Publication Date: 2012
Binding: hardcover
Condition: Good
Edition: Illustrated.
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Machine Learning: A Probabilistic Perspective (Adaptive Computation and Machine Learning series)
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Machine Learning: A Probabilistic Perspective (Adaptive computation and machine learning.)
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Machine Learning: A Probabilistic Perspective
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Machine Learning: A Probabilistic Perspective
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Machine Learning : A Probabilistic Perspective
Published by
The MIT Press, Cambridge, Massachusetts, 2012
ISBN 10: 0262018020
ISBN 13: 9780262018029
Used
Hardcover
Seller: Barnaby, Oxford, United Kingdom
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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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