Probabilistic Machine Learning: An Introduction, 確率的機械学習: 上級トピックス, 9780262048439, 978-0-262-04843-9

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Probabilistic Machine Learning: Advanced Topics
確率的機械学習: 上級トピックス
著者・編者 Murphy, K.P.
発行元 The MIT Press
発行年/月 2023年8月   
装丁 Hardcover
ページ数 1360 ページ
ISBN 978-0-262-04843-9
発送予定 1-2営業日以内に発送します


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