ISBN10: 0262018020, ISBN13: 9780262018029, [publisher: The MIT Press] Hardcover
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ISBN10: 0262018020, ISBN13: 9780262018029, [publisher: MIT Press Ltd, Cambridge, Mass.] Hardcover Hardcover. 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 ...

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ISBN10: 0262018020, ISBN13: 9780262018029, [publisher: The MIT Press] Hardcover
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Mit Press 2012 Hard cover New 2012. 1st Edition. Hardcover. Series: Adaptive Computation and Machine Learning Series. Num Pages: 1104 pages, 300 color illus., 165 b&w illus. BIC Classification: UYQM. Category: (G) General (US: Trade). Dimension: 241 x 213 x 44. Weight in Grams: 1958......We ship daily from our Bookshop.

Cambridge Mit Press 2012 Hard cover New. Sewn binding. Cloth over boards. 1104 p. Contains: Illustrations, black & white, Illustrations, color. Adaptive Computation and Machine Learning.

ISBN10: 0262018020, ISBN13: 9780262018029, [publisher: The MIT Press] Hardcover
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Cambridge Mit Press 2012 Hard cover New. Sewn binding. Cloth over boards. 1104 p. Contains: Illustrations, black & white, Illustrations, color. Adaptive Computation and Machine Learning.

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Mit Press 2012 Hard cover New, US edition. Satisfaction guaranteed! !

ISBN10: 0262018020, ISBN13: 9780262018029, [publisher: MIT Press Ltd, Cambridge, Mass.] Hardcover Hardcover. 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 ...
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ISBN10: 0262018020, ISBN13: 9780262018029, [publisher: MIT Press Ltd Aug 2012] Hardcover Neuware - 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 ...
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ISBN10: 0262018020, ISBN13: 9780262018029, [publisher: The MIT Press] Hardcover Book is in NEW condition.
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ISBN10: 0262018020, ISBN13: 9780262018029, [publisher: MIT Press] Hardcover New Book. Shipped from UK. Established seller since 2000.
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ISBN10: 0262018020, ISBN13: 9780262018029, [publisher: MIT Press Ltd Aug 2012] Hardcover Neuware - 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 ...
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