000			01201nam a2200217 4500
005			20250329134638.0
008			250329b |||||||| |||| 00| 0 eng d
020			_a9780262048439
037			_cTextual
040			_aRTL _cRTL
084			_aD6,9(B) R3 _qRTL
100			_aMurphy, Kevin P. _929210
245			_aProbabilistic Machine Learning
260			_aLondon _bThe MIT Press _c2023
300			_axxviii, 1319p. _bIncludes index and bibliography
520			_aThis 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 _9480917
650			_aInformation theory _9734016
650			_aProbabilities
942			_2CC _n0 _cTB _hD6,9(B) R3
999			_c1308290 _d1308290