First of all, as some other reviewers have pointed out, the subtitle of the book should include the word 'Bayesian' in some form or the other. The reason this is important is because the Bayesian approach, although an important one, is not adapted across the board in machine learning, and consequently, an astonishing number of methods presented in the book (Bayesian versions of just about anything) are not mainstream. The recent Duda book gives a better idea of the mainstream in this sense, but because the field has evolved in such rapidity, it excludes massive recent developments in kernel methods and graphical models, which Bishop includes.
Pedagogically, however, this book is almost uniformly excellent. I didn't like the presentation on some of the material (the first few sections on linear classification are relatively poor), but in general, Bishop does an amazing job. If you want to learn the mathematical base of most machine learning methods in a practical and reasonably rigorous way, this book is for you. Pay attention in particular to the exercises, which are the best I've seen so far in such a text; involved, but not frustrating, and always aiming to further elucidate the concepts. If you want to really learn the material presented, you should, at the very least, solve all the exercises that appear in the sections of the text (about half of the total). I've gone through almost the entire text, and done just that, so I can say that it's not as daunting as it looks. To judge your level regarding this, solve the exercises for the first two chapters (the second, a sort of crash course on probability, is quite formidable). If you can do these, you should be fine. The author has solutions for a lot of them on his website, so you can go there and check if you get stuck on some.
As far as the Bayesian methods are concerned, they are usually a lot more mathematically involved than their counterparts, so solving the equations representing them can only give you more practice. Seeing the same material in a different light can never hurt you, and I learned some important statistical/mathematical concepts from the book that I'd never heard of, such as the Laplace and Evidence Approximations. Of course, if you're not interested, you can simply skip the method altogether.
From the preceding, it should be clear that the book is written for a certain kind of reader in mind. It is not for people who want a quick introduction to some method without the gory details behind its mathematical machinery. There is no pseudocode. The book assumes that once you get the math, the algorithm to implement the method should either become completely clear, or in the case of some more complicated methods (SVMs for example), you know where to head for details on an implementation. Therefore, the people who will benefit most from the book are those who will either be doing research in this area, or will be implementing the methods in detail on lower level languages (such as C). I know that sounds offputting, but the good thing is that the level of the math required to understand the methods is quite low; basic probability, linear algebra and multivariable calculus. (Read the appendices in detail as well.) No knowledge is needed, for example, of measure-theoretic probability or function spaces (for kernel methods) etc. Therefore the book is accessible to most with a decent engineering background, who are willing to work through it. If you're one of the people who the book is aimed at, you should seriously consider getting it.
Edited to Add:
I've changed my rating from 4 stars to 5. Even now, 4-5 years later, there is simply no good substitute for this book.
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Customers find this machine learning book comprehensive and well-written, serving as a great reference for theoretical aspects. The content is in-depth and suitable for both beginners and professionals, though readability and clarity receive mixed feedback - while some find it easy to read and clear, others find it hard to understand some points. Customers disagree on the difficulty level, with some appreciating the math content while others find it challenging.
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99 customers mention content, 84 positive, 15 negative
...follow (forward-backward algorithm for example), but overall was is good book and worth getting....Read more
This is really an excellent book. It is very clear and there is a excellent balance...Read more
I can appreciate others who might think that this is a great book.... but I am a student using it and I have some very different opinions of it....Read more
Pretty much the bible of data science. Great depth and range.Read more
54 customers mention reference, 47 positive, 7 negative
...That said as a warning, now to the book. It is a great reference and spends a great deal of time several statistical theories and their practical...Read more
Authoritative reference ... though very dense on information but very readable ...Read more
...compulsory book required by statistical machine learning, good for the course and research, but not recommend for practical machine learning.Read more
Useful book and reasonable price. There're some pencil marks inside but doesn't bother me at all.Read more
17 customers mention writing quality, 12 positive, 5 negative
I think this is a great text considering the vast array of topics covered by Bishop in this book....Read more
...Bishop is absolutely clear, and an excellent writer as well....Read more
The book is badly written....Read more
The book is written very well. It is not to hard to read, explained in an interesting and clear way and at the same time high quality.Read more
7 customers mention comprehensiveness, 6 positive, 1 negative
A lot of symbols and a lot of pages but not a lot of insight in either the discussion or the problems....Read more
...This book emphasizes a "conceptual" approach to teaching pattern recognition, and therefore is highly valuable to those who need to learn the subject...Read more
This is a great reference on Pattern Recognition and, clearly, is based on others classical books on this theme (Richard O. Duda, for instance)....Read more
This new book by Chris Bishop covers most areas of pattern recognition quite exhaustively....Read more
25 customers mention readability, 11 positive, 14 negative
This is a decent book, but it is a hard readRead more
...It covered much of the topics I've seen It's very readable (even for non-english native speakers) and math is also well explained.Read more
Book is great and is the standard on the subject matter. It's a dense read, but that's to be expected.Read more
...various methods and their usage limitations, sometimes in very easy to understand language and studded with some real practical experience going...Read more
24 customers mention clarity, 12 positive, 12 negative
...It is not to hard to read, explained in an interesting and clear way and at the same time high quality.Read more
...However, the book is a bit advanced for an initial exposure to the material, and the perspective is very strongly Bayesian....Read more
...the contents are abundant, and full explanation and exercises will lead you into the field.Read more
...To make matter worse, he occasionally uses symbols that are flat-out confusing. Why would you use PI for anything other than Pi or Product? He does....Read more
11 customers mention difficulty level, 7 positive, 4 negative
...methods is quite low; basic probability, linear algebra and multivariable calculus. (Read the appendices in detail as well.)...Read more
...But, the Math used throughout the book is quite difficult and the author didn't address the theory behind the math with the rigor expected and...Read more
...or other quantitative backgrounds from readers, as it jumps right into advanced calculus, linear algebra and optimization without much explanation....Read more
...like in "NNPR", the writing style here is very clear, and everything past basic calculus and linear algebra is well-explained before it's needed....Read more
6 customers mention conciseness, 2 positive, 4 negative
...Extremely wordy, and lacks organization. There is too much "boring" equations and not enough "cool math" (geometric intuitions)....Read more
...presents the mathematical derivations of each technique clearly and concisely....Read more
...At the same time some parts feels a bit too wordy. Overall still a satisfying book to have, especially if you use it as course materialRead more
...your time - you can find dense, technically correct but incomprehensible descriptions for any of these methods online, for free....Read more
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- prmlReviewed in Australia on January 23, 2023
Amazingly written, fantastic print quality.
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- Bearded BoyReviewed in Canada on February 17, 2016
this book deserves to be on your shelf
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