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Item specifics

Condition
New: A new, unread, unused book in perfect condition with no missing or damaged pages. See the ...
Book Title
Machine Learning : A Probabilistic Perspective
ISBN
9780262018029

About this product

Product Identifiers

Publisher
MIT Press
ISBN-10
0262018020
ISBN-13
9780262018029
eBay Product ID (ePID)
117365328

Product Key Features

Number of Pages
1104 Pages
Language
English
Publication Name
Machine Learning : a Probabilistic Perspective
Subject
Algebra / Linear, Probability & Statistics / General, Computer Vision & Pattern Recognition
Publication Year
2012
Type
Textbook
Author
Kevin P. Murphy
Subject Area
Mathematics, Computers
Series
Adaptive Computation and Machine Learning Ser.
Format
Hardcover

Dimensions

Item Height
1.8 in
Item Weight
67.8 Oz
Item Length
9.3 in
Item Width
8.4 in

Additional Product Features

Intended Audience
Trade
LCCN
2012-004558
Reviews
This comprehensive book should be of great interest to learners and practitioners inthe field of machine learning., "This comprehensive book should be of great interest to learners and practitioners inthe field of machine learning." -- British Computer Society, This comprehensive book should be of great interest to learners and practitioners in the field of machine learning., This comprehensive book should be of great interest to learners and practitioners in the field of machine learning.-- British Computer Society --
Dewey Edition
23
Illustrated
Yes
Dewey Decimal
006.3/1
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 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., 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.
LC Classification Number
Q325.5.M87 2012

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