000			02137cam a22003255i 4500
003			EG-CaNGU
005			20240131131641.0
008			231122t20232022enka frb 001 0 eng d
020			_a9780262046824 _qAn introduction (hardcover)
020			_a9780262048439 _qAdvanced topics (hardcover)
040			_aDLC _beng _erda _cDLC _dOCLCO _dOCLCF _dUKMGB _dWAU _dYDX _dOCLCO _dEG-CaNGU
082	0	4	_a006.31 _bMUP _223
100	1		_aMurphy, Kevin P., _d1970-, _eauthor. _95931
245	1	0	_aProbabilistic machine learning /
264		1	_aCambridge, England : _bThe MIT Press, _c[2022 - 2023]
264		4	_c©2022 - 2023
300			_a2 volumes (various paging) : _billustrations ; _c24 cm.
336			_atext _btxt _2rdacontent
337			_aunmediated _bn _2rdamedia
338			_avolume _bnc _2rdacarrier
490	0		_aAdaptive computation and machine learning series
504			_aIncludes bibliographical references and index.
505	0	0	_aAn Introduction: I Introduction -- II Linear Models -- III Deep Neural Networks -- IV Nonparametric Models -- V Beyond Supervised Learning.
505	0	0	_aAdvanced Topics: I Introduction -- II Inference -- III Prediction -- IV Generation -- V Discovery -- VI Action.
520			_a"This book provides a detailed and up-to-date coverage of machine learning. It is unique in that it unifies approaches based on deep learning with approaches based on probabilistic modeling and inference. It provides mathematical background (e.g. linear algebra, optimization), basic topics (e.g., linear and logistic regression, deep neural networks), as well as more advanced topics (e.g., Gaussian processes). It provides a perfect introduction for people who want to understand cutting edge work in top machine learning conferences such as NeurIPS, ICML and ICLR"-- _cProvided by publisher.
650		7	_aMachine learning. _2NGU-sh _93897
650		7	_aProbabilities. _2NGU-sh _93770
999			_c1971 _d1971