Probabilistic Machine Learning by Kevin P Murphy

Probabilistic Machine Learning by Kevin P Murphy

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

SKU	Unavailable
ISBN 13	9780262048439
ISBN 10	0262048434
Title	Probabilistic Machine Learning
Author	Kevin P Murphy
Series	Adaptive Computation And Machine Learning Ser
Condition	Unavailable
Binding Type	Hardback
Publisher	MIT Press Ltd
Year published	2023-08-15
Number of pages	1360
Cover note	Book picture is for illustrative purposes only, actual binding, cover or edition may vary.