• Title: Probabilistic Machine Learning: Advanced Topics
• Author(s) Kevin Patrick Murphy
• Publisher: The MIT Press (August 15, 2023); eBook (Creative Commons Licensed, 2024)
• License(s): Creative Commons License (CC)
• Hardcover: 1360 pages
• eBook: PDF (1369 pages, 152 MB)
• Language: English
• ASIN: B0BMKHP4YG
• ISBN-10: 0262048434
• ISBN-13: 978-0262048439
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Book Description

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.

• Kevin P. Murphy is a Research Scientist at Google in Mountain View, California, where he works on artificial intelligence, machine learning, and Bayesian modeling.

• Amazon
• Amazon (Probabilistic Deep Learning with Python)

• Machine Learning
• Probability Theory and Stochastic Process
• Python Programming
• Artificial Intelligence

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

• Probabilistic Machine Learning: An Introduction (Kevin Murphy)

  This book is a comprehensive introduction to machine learning that uses probabilistic models and inference as a unifying approach. It is written in an informal, accessible style, complete with pseudo-code for the most important algorithms.

• Machine Learning: A Probabilistic Perspective (Kevin Murphy)

  This textbook offers a comprehensive and self-contained introduction to the field of machine learning, based on a unified, probabilistic approach. It is written in an informal, accessible style, complete with pseudo-code for the most important algorithms.

• An Introduction to Statistical Learning (Gareth James, et al)

  It provides an accessible overview of the field of Statistical Learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years.

• Probabilistic Machine Learning for Civil Engineers

  This comprehensive textbook presents basic machine learning methods for civil engineers who do not have a specialized background in statistics or in computer science. It includes several case studies that students and professionals will appreciate.

• Bayesian Methods for Hackers: Probabilistic Programming

  This book illuminates Bayesian inference through probabilistic programming with the powerful PyMC language and the closely related Python tools NumPy, SciPy, Matplotlib, through practical examples and computation - no advanced mathematics required.

• Pattern Recognition and Machine Learning (Christopher Bishop)

  This is the first textbook on pattern recognition to present the Bayesian viewpoint. The book presents approximate inference algorithms that permit fast approximate answers in situations where exact answers are not feasible.

• Foundations of Machine Learning (Mehryar Mohri, et al)

  This book is a general introduction to machine learning. It covers fundamental modern topics in machine learning while providing the theoretical basis and conceptual tools needed for the discussion and justification of algorithms.

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

  Everything you really need to know in Machine Learning in a hundred pages! This book provides a great practical guide to get started and execute on ML within a few days without necessarily knowing much about ML apriori.