Probabilistic Machine Learning

Probabilistic Machine Learning Advanced Topics - Adaptive Computation and Machine Learning

Hardback (10 Aug 2023)

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Publisher's Synopsis

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 distributions Explores how to use probabilistic models and inference for causal inference and decision making Features online Python code accompaniment

Book information

ISBN: 9780262048439
Publisher: The MIT Press
Imprint: The MIT Press
Pub date:
DEWEY: 006.31015192
DEWEY edition: 23
Number of pages: 1360
Weight: 2312g
Height: 213mm
Width: 237mm
Spine width: 55mm