000			01651cam a2200301 i 4500
001			22069826
005			20230815143204.0
008			210607s2022 mau b 001 0 eng
020			_a9780262046824 _q(hardcover)
040			_aDLC _beng _cEC-QuPUC _erda _dEC-QuPUC
041			_aenm
042			_apcc
082	0	0	_a006.31 _bM954p _223
100			_aMurphy, Kevin P., _977254 _eorg
245	1	0	_aProbabilistic machine learning : _ban introduction / _cKevin P. Murphy.
264		1	_aCambridge, Massachusetts : _bThe MIT Press, _c2022
300			_axxix, 826 pages : _billustrations (some color) ; _c24 cm
336			_atxt
337			_an
338			_anc
490	0		_aAdaptive computation and machine learning series _977255
504			_aIncludes bibliographical references and index.
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"--
650		7	_aAprendizaje automático (Inteligencia artificial) _2 _945327
650		7	_aProbabilidades _962163
856			_uhttps://puce.odilo.us/info/probabilistic-machine-learning-an-introduction-03131947
942			_cBK _00
999			_c283425 _d283425