• Title: Probabilistic Machine Learning: Advanced Topics
• Author(s) Kevin Patrick Murphy
• Publisher: The MIT Press (March, 2023); eBook (Draft, Creative Commons Licensed)
• License(s): CC-BY-NC-ND
• Hardcover: ???
• eBook: PDF (1352 pages, 138 MB)
• Language: English
• ASIN: N/A
• ISBN-10: N/A
• ISBN-13: 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.

• Kevin Patrick Murphy is a Research Scientist at Google.

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• Machine Learning
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• Artificial Intelligence

• Probabilistic Machine Learning: Advanced Topics (Kevin Patrick Murphy)
• Book Series

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• The Hundred-Page Machine Learning Book (Andriy Burkov)

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