000			01544nam a22002537a 4500
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008			250626b |||||||| |||| 00| 0 eng d
020			_a9780262048439
040			_aCSL _cCSL
041			_2eng _aeng
084			_aD65,8(B):(S:72) R3 _qCSL
100			_aMurphy, Kevin P _eauthor. _9814693
245			_aProbabilistic Machine Learning _b: Advanced Topics
260			_aLondon : _bMIT Press, _c2023.
300			_axxxi,1319p. _b: col.ill. _c; 23 cm.
500			_aIncludes bibliography and index.
520			_aAn advanced counterpart to Probabilistic Machine Learning: An Introduction, this high-level textbook provides researchers and graduate students detailed coverage of cutting-edge topics in machine learning, including deep generative modeling, graphical models, Bayesian inference, reinforcement learning, and causality. This volume puts deep learning into a larger statistical context and unifies approaches based on deep learning with ones based on probabilistic modeling and inference. With contributions from top scientists and domain experts from places such as Google, DeepMind, Amazon, Purdue University, NYU, and the University of Washington, this rigorous book is essential to understanding the vital issues in machine learning.
650			_aStatistics.
650			_aGraphical models. _9734433
650			_a Variational inference. _9814694
650			_aGenerative models. _9814695
650			_aCausation.
942			_2CC _cTEXL _hD65,8(B):(S:72) R3 _n0
999			_c1432992 _d1432992