Description
| Product ID: | 9780262048439 |
| Product Form: | Hardback |
| Country of Manufacture: | US |
| Title: | Probabilistic Machine Learning |
| Subtitle: | Advanced Topics |
| Authors: | Author: Kevin P. Murphy |
| Page Count: | 1360 |
| Subjects: | Information technology: general topics, Information technology: general issues |
| Description: | Select Guide Rating 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.
|
| Imprint Name: | MIT Press |
| Publisher Name: | MIT Press Ltd |
| Country of Publication: | GB |
| Publishing Date: | 2023-08-15 |
