Machine Learning: A Probabilistic Perspective
Kevin P. Murphy
Sold by Mesquite Booksellers, Tucson, AZ, U.S.A.
AbeBooks Seller since 4 December 2025
Used - Hardcover
Condition: Used - Very good
Ships within U.S.A.
Quantity: 1 available
Add to basketSold by Mesquite Booksellers, Tucson, AZ, U.S.A.
AbeBooks Seller since 4 December 2025
Condition: Used - Very good
Quantity: 1 available
Add to basketxxx,1067. 1950 grams. No jacket as issued. Light to mild ordinary shelfwear to the boards and edges. Dust at the top edge. Pages are crisp and entirely unmarked. It weighs almost 2 kilos. If you order from outside the United States, we will almost certainly request an additional payment to help cover the postage. Please feel welcome to message us with any questions, or a request for photographs. Every order includes tracking and is wrapped and robustly packaged with care in Tucson, AZ. ~Mesquite Booksellers.
Seller Inventory # 75500
A comprehensive introduction to machine learning that uses probabilistic models and inference as a unifying approach.
Today's Web-enabled deluge of electronic data calls for automated methods of data analysis. Machine learning provides these, developing methods that can automatically detect patterns in data and then use the uncovered patterns to predict future data. This textbook offers a comprehensive and self-contained introduction to the field of machine learning, based on a unified, probabilistic 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 pseudo-code 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 model-based 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 a MATLAB software package―PMTK (probabilistic modeling toolkit)―that is freely available online. The book is suitable for upper-level undergraduates with an introductory-level college math background and beginning graduate students.
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