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Adaptive Computation and Machine Learning Ser.: Machine Learning : A Probabilistic Perspective by Kevin P. Murphy (2012, Hardcover)
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- Buy It NowMachine Learning : A Probabilistic Perspective by Kevin Murphy.
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About this product
Product Identifiers
PublisherMIT Press
ISBN-100262018020
ISBN-139780262018029
eBay Product ID (ePID)117365328
Product Key Features
Number of Pages1104 Pages
Publication NameMachine Learning : a Probabilistic Perspective
LanguageEnglish
Publication Year2012
SubjectAlgebra / Linear, Probability & Statistics / General, Computer Vision & Pattern Recognition
TypeTextbook
Subject AreaMathematics, Computers
AuthorKevin P. Murphy
SeriesAdaptive Computation and Machine Learning Ser.
FormatHardcover
Dimensions
Item Height1.8 in
Item Weight67.8 Oz
Item Length9.3 in
Item Width8.4 in
Additional Product Features
Intended AudienceTrade
LCCN2012-004558
Dewey Edition23
ReviewsThis 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 --
IllustratedYes
Dewey Decimal006.3/1
SynopsisA 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 NumberQ325.5.M87 2012
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- Aug 29, 2018
Starting reading
Recently started reading it. It looks greatVerified purchase: YesCondition: New
- Jul 18, 2018
good and fast
good and fastVerified purchase: YesCondition: Pre-owned
