Probabilistic Machine Learning: Advanced Topics by Kevin P. Murphy (English) Har

Description: Probabilistic Machine Learning by Kevin P. Murphy "An advanced book for researchers and graduate students working in machine learning and statistics that reflects the influence of deep learning"-- FORMAT Hardcover LANGUAGE English CONDITION Brand New Publisher Description An 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 distributionsExplores how to use probabilistic models and inference for causal inference and decision makingFeatures online Python code accompaniment Author Biography Kevin P. Murphy is a Research Scientist at Google in Mountain View, California, where he works on artificial intelligence, machine learning, and Bayesian modeling. Table of Contents 1 Introduction 1I Fundamentals 32 Probability 53 Statistics 634 Graphical models 1435 Information theory 2176 Optimization 255II Inference 3377 Inference algorithms: an overview 3398 Gaussian filtering and smoothing 3539 Message passing algorithms 39510 Variational inference 43311 Monte Carlo methods 47712 Markov chain Monte Carlo 49313 Sequential Monte Carlo 537III Prediction 56714 Predictive models: an overview 56915 Generalized linear models 58316 Deep neural networks 62317 Bayesian neural networks 63918 Gaussian processes 67319 Beyond the iid assumption 727IV Generation 76320 Generative models: an overview 76521 Variational autoencoders 78122 Autoregressive models 81123 Normalizing flows 81924 Energy-based models 83925 Diffusion models 85726 Generative adversarial networks 883V Discovery 91527 Discovery methods: an overview 91728 Latent factor models 91929 State-space models 96930 Graph learning 103131 Nonparametric Bayesian models 103532 Representation learning 103733 Interpretability 1061VI Action 109134 Decision making under uncertainty 109335 Reinforcement learning 113336 Causality 1171 Details ISBN0262048434 Author Kevin P. Murphy Short Title Probabilistic Machine Learning Language English Year 2023 ISBN-10 0262048434 ISBN-13 9780262048439 Format Hardcover Publication Date 2023-08-15 Pages 1360 Publisher MIT Press Ltd Series Adaptive Computation and Machine Learning series Imprint MIT Press Country of Publication United States AU Release Date 2023-08-15 NZ Release Date 2023-08-15 US Release Date 2023-08-15 UK Release Date 2023-08-15 Illustrations 350 COLOR ILLUS. DEWEY 006.31015192 Audience General Subtitle Advanced Topics We've got this At The Nile, if you're looking for it, we've got it. With fast shipping, low prices, friendly service and well over a million items - you're bound to find what you want, at a price you'll love! TheNile_Item_ID:145230755;

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