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Probabilistic Machine Learning
Kevin P. Murphy
其他書名
Advanced Topics
出版
MIT Press
, 2023-08-15
主題
Computers / Data Science / Machine Learning
Computers / Computer Science
Computers / Artificial Intelligence / General
ISBN
0262376008
9780262376006
URL
http://books.google.com.hk/books?id=Bi2cEAAAQBAJ&hl=&source=gbs_api
EBook
SAMPLE
註釋
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