000			01992nam a2200205 4500
005			20250105120218.0
008			250105b |||||||| |||| 00| 0 eng d
020			_a9780262048439
082			_a006.31015192 _bMUR
100			_aMurphy, Kevin P _917982
245			_aProbabilistic machine learning: _badvanced topics
260			_bThe MIT press _aCambridge _c2023
300			_axxxi, 1319 p.
365			_aUSD _b150.00
490			_aAdaptive Computation and Machine Learning
520			_aAn advanced book for researchers and graduate students working in machine learning and statistics who want to learn about deep learning, Bayesian inference, generative models, and decision making under uncertainty. An 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. • Covers generation of high dimensional outputs, such as images, text, and graphs • Discusses methods for discovering insights about data, based on latent variable models • Considers training and testing under different distributions • Explores how to use probabilistic models and inference for causal inference and decision making • Features online Python code accompaniment (https://mitpress.mit.edu/9780262048439/probabilistic-machine-learning/)
650			_aMachine learning
650			_aProbabilities
942			_cBK _2ddc
999			_c7426 _d7426