000			02053cam a22002655a 4500
001			17212088
005			20201128021456.0
008			120315t2012 maua frb 001 0 eng d
020			_a9780262018029 (hardcover : alk. paper)
040			_aDLC _beng _cDLC _dEG-ScBUE _dEG-ScBUE
082	0	4	_a006.31 _bMUR _222
100	1		_aMurphy, Kevin P., _d1970- _938852
245	1	0	_aMachine learning : _ba probabilistic perspective / _cKevin P. Murphy.
260			_aCambridge, Massachusetts : _bMassachusetts Institute of Technology (The MIT Press) , _cc.2012.
300			_axxix, 1071 p. : _bill. (some col.) ; _c24 cm.
490	0		_aAdaptive computation and machine learning
500			_aIndex : p.1051-1071.
504			_aBibliography : p. 1019- 1050.
520			_aThis 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.
650		7	_aMachine learning. _2BUEsh _92922
650		7	_aProbabilities. _2BUEsh _93494
651			_2BUEsh
653			_bCOMSCI _cAugust2015 _cDecember2015 _cJanuary2016
942			_2ddc _k006.31 MUR
999			_c20482 _d20454