Machine learning : a probabilistic perspective

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 sel...

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Hlavní autor: Murphy, Kevin P., 1970-
Médium: Livre papier
Jazyk:Anglais
Vydáno: Cambridge (Mass.) ; London : The MIT Press C 2012.
Edice:Adaptive computation and machine learning
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Edition sous un autre format:• Machine learning, a probabilistic perspective, Kevin P. Murphy, 2012, Cambridge, Massachusetts, The MIT Press, Adaptive computation and machine learning series, 978-0-262-30524-2
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245 1 0 |a Machine learning :  |b a probabilistic perspective   |c Kevin P. Murphy. 
260 |a Cambridge (Mass.) ;  |a London :  |b The MIT Press. 
260 |c C 2012. 
300 |a 1 vol. (xxix-1071 p.). :  |b ill. en noir et en coul., couv. ill. en coul. ;  |c 24 cm. 
490 0 |a Adaptive computation and machine learning 
504 |a Bibliogr. p. [1019]-1050. Index 
520 |a 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. 
650 |a Apprentissage automatique 
650 |a Probabilités 
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