Pattern Recognition and Machine Learning (Information Science and Statistics)

There are a huge number of machine learning books now available. I own many of them. But I don't think any have had such an impact as Chris Bishop's effort here - I certainly count it as my favourite. The material covered is not exhaustive (although good for 2006), but it's a good springboard to many other advanced texts. (The moniker of ML 'Bible' has apparently been passed to Kevin Murphy's book.) What *is* covered is explained with exceptional clarity with an eye for understanding the intuition as well as the theory. If you are after a practitioners guide, or a first ML book for self study, this probably isn't ideal. It assumes significant familiarity with multivariate calculus, probability and basic stats (identities, moments, regression, MLE etc.). The pitch is probably early post-graduate level, but with a few stretching parts. If this is your background, I think it's a better first ML book than MacKay (Information Theory ...), Murphy (Machine Learning ...), or Hastie et al. (Elements of Statistical Learning), due to its coherence of topics and consistency of depth. But those books are all excellent in their own ways. However, Barber (Bayesian Reasoning ...) is a good alternative. Most chapters are fairly self contained, so once you've worked your way through the first couple of chapters, you can skip around as required. A particular highlight for me were the chapters on EM and variational methods (ch 9 & 10); I think you'd be hard pressed to find a better explanation of either of them. Finally, worth pointing out it's unrepentantly Bayesian, and lacking some subtelty which may be grating for seasoned statisticians. Nevertheless, if the above sounds like what you're looking for, this is probably a good choice.

This is a great book with one of the most clear presentations of several fundamental algorithms. In my experience this is a book I keep coming back to.

It's one of the best if not the best book for theory in machine learning. It's readable and very comprehensible for someone who has a mathematical background.

Although it's expensive book I think it worth the money as it is the "Bible" of Machine Learning and Pattern recognition. However, has a lot of mathematics meaning that a strong mathematical background is necessary. I suggest it especially for PhD candidates in this field.

It's a must get for Machine Learning students. It covers every fundamental concept of ML. However, it is not quite beginner level friendly, meaning you are required to have some understanding of basic probability and linear algebra. I am giving four stars due to the way it's printed. The print paper quality is good and I can confirm it is hardcover but the margin is bit unusual with wide space on the left hand side.

I take back my previous negative review (DHL returned without delivering to me for some reason not explained). I received the book today and very happy - exactly as expected - excellent quality!

a reference in the domain of machine learning ... plus the quality of the paper used, the colors .... everything makes this book a must have if you are interested in machine learning

Great monography about statistic computation and modern pattern recognition. Timeless book.

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.

Habe das Buch bestellt, alles super funktioniert.

This book is excellently written. It is not simply a reference bible, the author tells a chronological story and takes you along for the ride. The print quality of my copy is excellent, nice waxy paper, crisp text and nice and colourful. As you've probably read elsewhere online, you will need to have done prior courses in probability and linear algebra, as the introductory chapters here, although technically "self contained", are very dense. Although Kevin Murphy's new 2022 book is also great, it feels like more of a reference on a zillion topics. Whereas with PMRL, Bishop is really trying to get you to understand the fundamentals.

素晴らしい本です。 パターン認識の教科書として、非常に優れていると思います。 パターン認識の原理や特徴、既存の有用な手法などが分かりやすく書かれています。 これらは統計の知識を駆使していますが、その基本の部分から書かれているので 独習する事も可能です。 また、フルカラーなので、グラフや図が非常に綺麗で見やすいです。 パターン認識を研究する初・中級者向けの本と言えると思います。

It’s a good introduction to the theory of machine learning. The math requirements are low, mostly undergrad algebra, and the calculations are easy to follow; needed probability concepts are explained in the first chapter. The book is quite exhaustive and covers all common ML techniques in detail.