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Machine learning : a probabilistic perspective / Kevin P. Murphy.

By: Murphy, Kevin P, 1970-.
Material type: materialTypeLabelBookSeries: Adaptive computation and machine learning.Publisher: Cambridge, Massachusetts : Massachusetts Institute of Technology (The MIT Press) , c.2012Description: xxix, 1071 p. : ill. (some col.) ; 24 cm.ISBN: 9780262018029 (hardcover : alk. paper).Subject(s): Machine learning | Probabilities | | Informatics and Computer Science August2015 December2015 January2016DDC classification: 006.31 Summary: 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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Item type Current location Collection Call number Vol info Status Date due Barcode Item holds
Book - Borrowing Book - Borrowing Central Library
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Baccah 006.31 MUR (Browse shelf) 21782 Checked out 24/09/2018 000030765
Book - Borrowing Book - Borrowing Central Library
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Baccah 006.31 MUR (Browse shelf) 21782 Available 000030805
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Index : p.1051-1071.

Bibliography : p. 1019- 1050.

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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