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 Title: Probabilistic Machine Learning: Advanced Topics
 Author(s) Kevin Patrick Murphy
 Publisher: The MIT Press (March, 2023); eBook (Draft, Creative Commons Licensed)
 License(s): CCBYNCND
 Hardcover: ???
 eBook: PDF (1352 pages, 138 MB)
 Language: English
 ASIN: N/A
 ISBN10: N/A
 ISBN13: N/A
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Book Description
In this book, we expand the scope of Machine Learning to encompass more challenging problems. For example, we consider training and testing under different distributions; we consider generation of high dimensional outputs, such as images, text and graphs, so the output space is, say, Y = R256×256; we discuss methods for discovering 'insights' about data, based on latent variable models; and we discuss how to use probabilistic models for causal inference and decision making under uncertainty.
We assume the reader has some prior exposure to ML and other relevant mathematical topics (e.g., probability, statistics, linear algebra, optimization).
Python code (mostly in JAX) to reproduce nearly all of the figures can be found online.
About the Authors Kevin Patrick Murphy is a Research Scientist at Google.
 Machine Learning
 Probability Theory and Stochastic Process
 Python Programming
 Artificial Intelligence

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