Probabilistic Machine Learning
1360 pp., 8 x 9 in, 350 color illus.
- Published: August 15, 2023
- Publisher: The MIT Press
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
Kevin Murphy has already greatly benefited the machine learning community with his introductory book, and I am delighted to see the depth and breadth of material in his sequel on advanced topics.
Yoshua Bengio, Professor of Computer Science and Operational Research, University of Montreal
This book distills the literature on machine learning and neural networks into a wonderful resource for both new students and seasoned researchers. The chapter on generative models is a masterpiece.
Geoffrey Hinton, Emeritus Professor of Computer Science, University of Toronto; Engineering Fellow, Google