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 Title: Probabilistic Machine Learning: An Introduction
 Author(s) Kevin Patrick Murphy
 Publisher: The MIT Press (March, 2022); eBook (Draft, Creative Commons Licensed, 2022)
 License(s): CCBYNCND
 Hardcover: 944 Pages
 eBook: PDF (855 pages, 87.6 MB)
 Language: English
 ASIN: B094X9M689
 ISBN10: 0262046822
 ISBN13: 9780262046824
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Book Description
This book is a comprehensive introduction to machine learning that uses probabilistic models and inference as a unifying approach.
The coverage combines breadth and depth, offering necessary background material on such topics as probability, optimization, and linear algebra as well as discussion of recent developments in the field, including conditional random fields, L1 regularization, and deep learning.
The book is written in an informal, accessible style, complete with pseudocode for the most important algorithms. All topics are copiously illustrated with color images and worked examples drawn from such application domains as biology, text processing, computer vision, and robotics.
Rather than providing a cookbook of different heuristic methods, the book stresses a principled modelbased approach, often using the language of graphical models to specify models in a concise and intuitive way.
Almost all the models described have been implemented in Python and is freely available 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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