Machine Learning: A Probabilistic Perspective Author: Visit Amazon's Kevin P. Murphy Page | Language: English | ISBN:
0262018020 | Format: EPUB
Machine Learning: A Probabilistic Perspective Description
Review
"An astonishing machine learning book: intuitive, full of examples, fun to read but still comprehensive, strong and deep! A great starting point for any university student -- and a must have for anybody in the field." --Jan Peters, Darmstadt University of Technology; Max-Planck Institute for Intelligent Systems
"Kevin Murphy excels at unraveling the complexities of machine learning methods while motivating the reader with a stream of illustrated examples and real world case studies. The accompanying software package includes source code for many of the figures, making it both easy and very tempting to dive in and explore these methods for yourself. A must-buy for anyone interested in machine learning or curious about how to extract useful knowledge from big data." --John Winn, Microsoft Research, Cambridge
"This is a wonderful book that starts with basic topics in statistical modeling, culminating in the most advanced topics. It provides both the theoretical foundations of probabilistic machine learning as well as practical tools, in the form of Matlab code. The book should be on the shelf of any student interested in the topic, and any practitioner working in the field."--Yoram Singer, Google Inc.
"This book will be an essential reference for practitioners of modern machine learning. It covers the basic concepts needed to understand the field as whole, and the powerful modern methods that build on those concepts. In Machine Learning, the language of probability and statistics reveals important connections between seemingly disparate algorithms and strategies. Thus, its readers will become articulate in a holistic view of the state-of-the-art and poised to build the next generation of machine learning algorithms." --David Blei, Princeton University
"This comprehensive book should be of great interest to learners and practitioners in the field of machine learning." -- British Computer Society
About the Author
Kevin P. Murphy is a Research Scientist at Google. Previously, he was Associate Professor of Computer Science and Statistics at the University of British Columbia.
- Series: Adaptive Computation and Machine Learning series
- Hardcover: 1104 pages
- Publisher: The MIT Press (August 24, 2012)
- Language: English
- ISBN-10: 0262018020
- ISBN-13: 978-0262018029
- Product Dimensions: 9.1 x 8.2 x 1.7 inches
- Shipping Weight: 4 pounds (View shipping rates and policies)
(Disclaimer: I have worked with a draft of the book and been allowed to use the instructor's review copy for this review. I have bought the book from Amazon.co.uk, but apparently this Amazon.com review can't be tagged "verified purchase". I don't receive any compensation whatsoever for writing this review. I hope it will help you chose a machine learning textbook.)
Similar textbooks on statistical/probabilistic machine learning (links to book websites, not Amazon pages):
- Barber's Bayesian Reasoning and Machine Learning ("BRML", Cambridge University Press 2012)
- Koller and Friedman's Probabilistic Graphical Models ("PGM", MIT Press 2009)
- Bishop's Pattern Recognition and Machine Learning ("PRML", Springer 2006)
- MacKay's Information Theory, Inference and Learning Algorithms ("ITILA", CUP 2003)
- Hastie, Tibshirani and Friedman's Elements of Statistical Learning ("ESL", Springer 2009)
* Perspective: My perspective is that of a machine learning researcher and student, who has used these books for reference and study, but not as classroom textbooks.
* Audience/prerequisites: they are comparable among all the textbooks mentioned. BRML has lower expected commitment and specialization, PGM requires more scrupulous reading. The books differ in their topics and disciplinary approach, some more statistical (ESL), some more Bayesian (PRML, ITILA), some focused on graphical models (PGM, BRML). K Murphy compares MLAPP to others here. For detailed coverage comparison, read the table of contents on the book websites.
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