PDF | Probabilistic Machine Learning – Advanced Topics

Probabilistic Machine Learning – Advanced Topics

Format: PDF eTextbooks
ISBN-13: 978-0262048439
ISBN-10: 0262048434
Delivery: Instant Download
Authors: Kevin P. Murphy
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 Washing