Machine Learning: A Probabilistic Perspective (Adaptive Computation and Machine Learning series) Illustrated Edition
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- List Price: $110.00
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.
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
- Publisher : The MIT Press; Illustrated edition (August 24, 2012)
- Language : English
- Hardcover : 1104 pages
- ISBN-10 : 0262018020
- ISBN-13 : 978-0262018029
- Reading age : 18 years and up
- Item Weight : 4.2 pounds
- Dimensions : 8.25 x 1.79 x 9.27 inches
- Best Sellers Rank: #245,971 in Books (See Top 100 in Books)
- Customer Reviews:
About the author
Reviewed in the United States on December 25, 2021
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Top reviews from the United States
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As a graduate student who had read a descent number of papers in the field, I feel very conflicted about this textbook.
If you expect to teach yourself machine learning from this textbook, this is in my opinion almost surely *not* the textbook to get. (0/5 Stars)
-The content of the textbook is highly disorganized. Future chapters are constantly referenced in the text (as if you have already read them!). Perplexingly, meaningful explanations of concepts are often delayed by multiple chapters. (Ex. BIC is introduced in Ch.6 but a mathematical justification is provided only in Ch. 8 when the mathematical justification could have (and should have) been in Ch. 6).
-A number of topics are merely mentioned (like VC dimension) but not actually discussed at any reasonable length, making some sections of the textbook meaningless.
-I would instead recommend the related (but different) text Introduction to Statistical Learning with Applications in R as it is quite accessible.
However, if you are an instructor and wish to use this textbook as a supplement to a course or are a researcher then Murphy's Machine Learning is in my opinion could be a worthwhile purchase. (4/5 stars)
-The examples, references and illustrations give the textbook a particularly nice touch. (I particularly enjoyed the example of calculating the posterior probability of user ratings of two different items on Amazon).
In summary, if you are an instructor that wants their students to learn how to read challenging exposition to prepare them for reading research papers in the field or if you wish to use this as a reference, then this is a good choice. Otherwise, pass.
(full disclosure: I'm a trained frequentist statistician) however with a coupe of years of experience in applied ML
as well as reading other ML/Stat. Learning books, I find that this book provides me with the most cohesive and
comprehensive framework for understanding and using ML algorithms/models. It has definitely become my
'gold standard' for reviewing ML related concepts. I find this author's notation and terminology the most useful
compared to other books. Also the author stresses the separation of the model from the algorithm. "This kind of
modularity, where we distinguish the model from the algorithms, is good pedagogy and good engineering" - the author. I agree.
However, for a newbie to ML, I would not recommend this book as your primary source. It can serve as a
secondary source or reference.
At a practical level, the book provides technical explains & provides solid technical rationale for a lot of modelling tricks I've used in the past, & suggested some very clever new ones.
This book is accessible for anyone with a background in reasonably advanced econometrics, say at level of W. H. Greene Econometrics (8th Ed, 2018).
On the whole a great book to have on your shelf if you are serious about AI, but not on its own. As far as the typos, they are pretty easy to spot so they were not a concern.
I don't find the book to be very readable or good for initially learning material from, but it does seem to have a pretty complete appraisal of the techniques composing each individual concept in ML, which is useful as a jumping off point for googling.
Top reviews from other countries
There are many typos in the first 3 printings. The 4th (and later) printing is much better. What I bought (11/24/2017) is the 6th printing (the same as the 4th).
2 Please note: The book mainly concentrate on various classic supervised and unsupervised learning methods, and not much on deep neural network (tons of materials online, e.g. youtube or tensorflow’s guide),and reinforcement learning (Sutton’s famous book).
3 The following sentences are what I found on Quora, said by the author (2016): “…the current version is admittedly rather hard for beginners. I am actually in the process of writing the second edition, which will ramp up more slowly, making it more accessible to beginners. (I'm also adding new content on deep learning, reinforcement learning, etc.) But it will take me a while to finish (~2 years?).”Before I bought it, I emailed the author to ask his plan on the 2nd edition. He said: “... I am working on it, but it won't be done till late 2018...”.
4 Last but not least, the book binding is firm, and, beautiful :)