000			nam a22 4500
999			_c10220 _d10220
005			20191203151558.0
008			191126b xxu||||| |||| 00| 0 eng d
020			_a9780262018029
040			_cAIKTC-KRRC
041			_aENG
082			_2DDC23 _bMUR _a006.31
100			_910681 _aMurphy, Kevin P.
245			_aMachine learning _b: A probabilistic perspective
260			_aLondon _bMIT Press _c2012
300			_axxix, 1071p. _bHard Bound _c23.5*20.8 cm
440			_910248 _aAdaptive computation and machine learning
520			_aoday'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. _bThe 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. The book is suitable for upper-level undergraduates with an introductory-level college math background and beginning graduate students.
650		0	_94619 _aEXTC Engineering
942			_2ddc _cBK