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Machine Learning: A Probabilistic Perspective (Adaptive Computation and Machine Learning series) 1st Edition

3.9 out of 5 stars 54 customer reviews
ISBN-13: 978-0262018029
ISBN-10: 0262018020
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Editorial Reviews

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.
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Product Details

  • Series: Adaptive Computation and Machine Learning series
  • Hardcover: 1104 pages
  • Publisher: The MIT Press; 1 edition (August 24, 2012)
  • Language: English
  • ISBN-10: 0262018020
  • ISBN-13: 978-0262018029
  • Product Dimensions: 8 x 1.4 x 9 inches
  • Shipping Weight: 4.6 pounds (View shipping rates and policies)
  • Average Customer Review: 3.9 out of 5 stars  See all reviews (54 customer reviews)
  • Amazon Best Sellers Rank: #80,544 in Books (See Top 100 in Books)

Customer Reviews

Top Customer Reviews

By Sebastien Bratieres on September 11, 2012
Format: Hardcover
(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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Format: Hardcover Verified Purchase
I'm sure if you are a Google Research Scientist and are not learning the material for the first time, this book is amazing. For everyone else, I would not recommend it. I bought this book for my Fall 2013 COMPSCI 571 class, and I regret it. Before buying this book, consider the following:

1. Take a look at the online Errata. This book is already in it's 3rd printing and it just came out. The list of corrections for this (the 3rd edition) is already mind-numbingly long. The 4th printing coming out this month will surely fix some errors, but there are just too many.
2. Our class has an online forum (for a 100 person class) where we discuss topics, and most questions are either (a) basic topics from the book that no one understood or (b) talking about how one figure in the book has multiple errors associated with it. At first I was really excited to find mistakes and submit them to the Errata - it was like I was part of the book! Now I just get frustrated and have already given up on submitting corrections.
3. Our instructor regrets using this book and modifies the examples before giving them to us in class. Our out of class readings now consist mostly of MetaAcademy.com.
4. There are hardly any worked-through examples, and many of those that are worked through have errors.
5. Many important concepts are skimmed over way too quickly. For example, there is a whole chapter on Logistic regression. However, Logistic regression is covered for exactly 2 pages.
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Format: Hardcover
For beginners:
A. For somewhat theoretical approach to machine learning
1. If you have less than a month to study it: Read Learning From Data (~$30).
2. If you have a semester: Read Learning From Data along with lecture series by Yaser's on youtube.

B. For more applied approach to machine learning
1. If you have semester: Go through Andrew Ng's lecture series

For intermediate to advanced:
1. If you have a semester: Read this book.

Other classic machine learning textbooks, if you have more time:
1. Pattern Recognition And Machine Learning (The first book I read on Machine Learning. Very accessible. More detailed than Yasir's book, but less than Kevin's book)
2. The Nature of Statistical Learning Theory (Information Science and Statistics) by Vapnik (One of the pioneer in this field. Extremely theoretical approach)
3. Elements of Statistical Learning - Hastie et al (free pdf copy available)
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Format: Hardcover Verified Purchase
This book was the textbook for the Machine Learning course I've taken and I can't say I found it very useful for learning the material on my own. This book is much more of a reference book than a self-learning book. It feels like the author's purpose was to include all the material in the field into a single, huge book. From that perspective, this is probably the most expansive book; you won't probably find any book talking about deep learning for example. However, when you're reading this book, you feel like the book was a bit rushed, it wasn't quite ready to be published. There is at least one typo in every page and a lot of them makes you wonder how that typo was missed. For example, all the algorithm references in text use wrong numbers.
One other thing, maybe again because it was a bit rushed, the book is not well organized. Most of the time you feel like the author took a bunch of sections written in different times and simply pasted them one after another. There is no coherent narrative that takes you through the text.
In short, although I appreciate the effort, I must say I'm disappointed with this book, especially given the hype about this book. I think this book needs some serious review, and first of all some proofreading.
8 Comments 71 people found this helpful. Was this review helpful to you? Yes No Sending feedback...
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