Machine learning : a probabilistic perspective
(Book)

Book Cover
Published:
Cambridge, MA : MIT Press, [2012].
Format:
Book
Physical Desc:
xxix, 1067 pages : illustrations (some color) ; 24 cm.
Status:
ASU Main (3rd floor)
Q325.5 .M87 2012
Copies
Location
Call Number
Status
ASU Main (3rd floor)
Q325.5 .M87 2012
On Shelf
Citations
APA Citation (style guide)

Murphy, K. P. (2012). Machine learning: a probabilistic perspective. Cambridge, MA: MIT Press.

Chicago / Turabian - Author Date Citation (style guide)

Murphy, Kevin P., 1970-. 2012. Machine Learning: A Probabilistic Perspective. Cambridge, MA: MIT Press.

Chicago / Turabian - Humanities Citation (style guide)

Murphy, Kevin P., 1970-, Machine Learning: A Probabilistic Perspective. Cambridge, MA: MIT Press, 2012.

MLA Citation (style guide)

Murphy, Kevin P. Machine Learning: A Probabilistic Perspective. Cambridge, MA: MIT Press, 2012. Print.

Note! Citation formats are based on standards as of July 2010. Citations contain only title, author, edition, publisher, and year published. Citations should be used as a guideline and should be double checked for accuracy.
Description

"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.

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Language:
English
ISBN:
9780262018029, 0262018020

Notes

Bibliography
Includes bibliographical references (pages [1015]-1045) and indexes.
Description
"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.
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