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.

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。

Target audience: Graduate students and researchers relatively new to the field of Bayesian learning. (+) Clearly written. High-quality print (figure quality is much higher than that of your average textbook). Fresh approach to HMMs and the Kalman Filter. Yes, the Kalman filter / smoother equations make much more sense when derived from a graphical model. A quick Google search will yield some accompanying lecture videos from the author (on graphical models and sequential learning). Solutions for the "www"-marked exercises are available from the author's webpage. (neutral) Formula-heavy so not for the faint of heart. (-) Little emphasis on computational / implementation aspects. There is no "official" (author's) code for the algorithms discussed in the book - however, there are some good-quality 3rd party implementations available on the web. Most of the exercises simply fill-in the missing steps in the algebra derivations. There are no coding exercises. Some of the data sets used in the book don't seem to be available anymore (at least not at the URL given in the book). Highly recommended. Best when used in conjunction with the 3rd party (MATLAB /Python) codes available on the web.

very comprehensive. will be relevant for a long time to come. there's a move for people to adopt the approach of learning coding libraries in order to solve problems...which is good but one still needs a reliable reference to fill in the blanks or to learn the basics (and advanced!).

Klasik bir kitap. Eski bir kitap olsa bile güncel araştırma konuların temellerini sağlam bir şekilde öğrenmek için en iyi kaynak. Kitaplığınızda bulunması iyidir. Orijinal ve hardcover.