000			01411nam a2200229 4500
005			20250508094955.0
008			250508b |||||||| |||| 00| 0 eng d
020			_a9780262046824
037			_cTextual
040			_aRTL _cRTL
084			_aD6,9(B) R2 _qRTL
100			_aMurphy, Kevin P. _929210
245			_aProbabilistic machine learning: An introduction
260			_aCambridge _bThe MIT Press _c2022
300			_axxix, 826 p. ill. _bIncludes bibliographical references and index
490			_aAdaptive computation and machine learning
520			_aA detailed and up-to-date introduction to machine learning, presented through the unifying lens of probabilistic modeling and Bayesian decision theory. This book offers a detailed and up-to-date introduction to machine learning (including deep learning) through the unifying lens of probabilistic modeling and Bayesian decision theory. The book covers mathematical background (including linear algebra and optimization), basic supervised learning (including linear and logistic regression and deep neural networks), as well as more advanced topics (including transfer learning and unsupervised learning). End-of-chapter exercises allow students to apply what they have learned, and an appendix covers notation.
650			_aMachine learning
650			_aProbabilities
650			_aStatistics
942			_2CC _n0 _cTB _hD6,9(B) R2
999			_c1320381 _d1320381