Pattern recognition has its origins in engineering, whereas machine learning grew out of computer science. However, these activities can be viewed as two facets of the same field, and together they have undergone substantial development over the past ten years. In particular, Bayesian methods have grown from a specialist niche to become mainstream, while graphical models h Pattern recognition has its origins in engineering, whereas machine learning grew out of computer science. However, these activities can be viewed as two facets of the same field, and together they have undergone substantial development over the past ten years. In particular, Bayesian methods have grown from a specialist niche to become mainstream, while graphical models have emerged as a general framework for describing and applying probabilistic models. Also, the practical applicability of Bayesian methods has been greatly enhanced through the development of a range of approximate inference algorithms such as variational Bayes and expectation propagation. Similarly, new models based on kernels have had a significant impact on both algorithms and applications. This new textbook reflects these recent developments while providing a comprehensive introduction to the fields of pattern recognition and machine learning. It is aimed at advanced undergraduates or first-year PhD students, as well as researchers and practitioners, and assumes no previous knowledge of pattern recognition or machine learning concepts. Knowledge of multivariate calculus and basic linear algebra is required, and some familiarity with probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory.

# Pattern Recognition and Machine Learning

Pattern recognition has its origins in engineering, whereas machine learning grew out of computer science. However, these activities can be viewed as two facets of the same field, and together they have undergone substantial development over the past ten years. In particular, Bayesian methods have grown from a specialist niche to become mainstream, while graphical models h Pattern recognition has its origins in engineering, whereas machine learning grew out of computer science. However, these activities can be viewed as two facets of the same field, and together they have undergone substantial development over the past ten years. In particular, Bayesian methods have grown from a specialist niche to become mainstream, while graphical models have emerged as a general framework for describing and applying probabilistic models. Also, the practical applicability of Bayesian methods has been greatly enhanced through the development of a range of approximate inference algorithms such as variational Bayes and expectation propagation. Similarly, new models based on kernels have had a significant impact on both algorithms and applications. This new textbook reflects these recent developments while providing a comprehensive introduction to the fields of pattern recognition and machine learning. It is aimed at advanced undergraduates or first-year PhD students, as well as researchers and practitioners, and assumes no previous knowledge of pattern recognition or machine learning concepts. Knowledge of multivariate calculus and basic linear algebra is required, and some familiarity with probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory.

Compare

4out of 5Nate–Even with the help of a nuclear physicists turned neurophysiology data analyst, I couldn’t work beyond the first four chapters, and perhaps only a percentage of those. However, the efforts are rewarding. If you have read the entirety of this book, and understand it, then I would very much like to replace part of my brain with yours.

4out of 5Manuel Antão–If you're into stuff like this, you can read the full review. Ropey Lemmings: "Pattern Recognition and Machine Learning" by Christopher M. Bishop As far as I can see Machine Learning is the equivalent of going in to B&Q and being told by the enthusiastic sales rep that the washing machine you are looking at is very popular (and therefore you should buy it too). Through clenched teeth I generally growl "That doesn't mean I think it is the best washing machine." Following the herd is not my bag; the If you're into stuff like this, you can read the full review. Ropey Lemmings: "Pattern Recognition and Machine Learning" by Christopher M. Bishop As far as I can see Machine Learning is the equivalent of going in to B&Q and being told by the enthusiastic sales rep that the washing machine you are looking at is very popular (and therefore you should buy it too). Through clenched teeth I generally growl "That doesn't mean I think it is the best washing machine." Following the herd is not my bag; there are enormous problems down the line: the circular argument of how people make choices is strengthening its grip as real-time information (likes and dislikes) accelerate across social media networks.

5out of 5Manny–Dave, who knows about these things, recommended it... I have just ordered a copy.

4out of 5Wooi Hen Yap–For beginners who need to understand Bayesian perspective on Machine Learning, I'd would say that's the best so far. The author has make good attempt to explain complicated theories in simplified manner by giving examples/applications. The best part of the book are chapters on graphical models (chapter 8), mixture model EM (chap 9) and approximate inference (chap 10). The reason I didn't give 5 stars because it is too narrow a perspective on Machine Learning (only from Bayesian Perspective) that For beginners who need to understand Bayesian perspective on Machine Learning, I'd would say that's the best so far. The author has make good attempt to explain complicated theories in simplified manner by giving examples/applications. The best part of the book are chapters on graphical models (chapter 8), mixture model EM (chap 9) and approximate inference (chap 10). The reason I didn't give 5 stars because it is too narrow a perspective on Machine Learning (only from Bayesian Perspective) that I feel did not terms well with the book title. Statistical learning and non-Bayesian perspective on machine learning are not covered much here. To make up for this discrepancies Tom Mitchell's Machine Learning does better job. Nevertheless, it still a great book to put on the shelve for machine learning.

5out of 5Oldrich–1. The book is mainly about Bayesian approach. And many important techniques are missing. This is the biggest problem I think. 2. “Inconsistent difficulty”, too much time spent on simple things and very short time spent on complicated stuff. 3. Lack of techniques demonstration on real world problems.

5out of 5Gavin–Timeless, towering. My yardstick: The first time I read it (looked at it) I was way out of my depth and understood little. Year by year I misunderstand less of it.

5out of 5Aasem Bakhshi–An amazing textbook that would never get old.

4out of 5Kjn–I must say this is a pretty painful read. Some parts seem to go very deep without much purpose, some topics which are pretty wide and important are skipped over in a paragraph. Maybe this book needs to go together with a taught course on the topic. On itself it is just too much.

4out of 5David–Being a new text, topics in modern machine learning research are covered. Bishop prefers intuitive explanations with lots of figures over mathematical rigor (Which is fine by me! =). A sample chapter is available at Bishop's website. Being a new text, topics in modern machine learning research are covered. Bishop prefers intuitive explanations with lots of figures over mathematical rigor (Which is fine by me! =). A sample chapter is available at Bishop's website.

4out of 5Van Huy–Took me a year to finish this book :D

5out of 5Fernando Flores–One of my first book on machine learning, this book can be painful if you don't have a solid background in algebra. One of my first book on machine learning, this book can be painful if you don't have a solid background in algebra.

5out of 5Emil Petersen–I started reading this book about 2 years too late, in my last year of my computer science degree. I have only now finished it, and I had to skim some of the last chapters. It's a pretty monumental task to read it through, and I cannot help but wonder how much it have taken to write it. Bishop has extraordinary insight into the Bayesian treatment in pattern recognition, and this is expressed here in, sometimes excruciating, details. If you're a beginner, I would just read the first 4 or so chapt I started reading this book about 2 years too late, in my last year of my computer science degree. I have only now finished it, and I had to skim some of the last chapters. It's a pretty monumental task to read it through, and I cannot help but wonder how much it have taken to write it. Bishop has extraordinary insight into the Bayesian treatment in pattern recognition, and this is expressed here in, sometimes excruciating, details. If you're a beginner, I would just read the first 4 or so chapters, maybe chapter 8 and skim some of the variational inference sections. For more advanced learners, the later chapters provide some excellent detail on how to go beyond the basics. I'm a little sad that this book was not a part of my official coursework, as I have only later discovered how relevant much of the content was for a significant part of my courses, and even worse, my thesis (where variational autoencoders and, hidden markov models and Bayesian ensemble models were at the center, all of which are either described directly in this book, or given foundation). The variational autoencoder would fit right in (which rose to prominence after the book was written). Chapter 5 on neural networks is good, but it feels disconnected from the rest of the book. Still, it's a good chapter in itself, and even though a lot is happening and has happened since the chapter was written, the foundations described here remain the same. People might use ReLU as activation now, and there are a few new tricks, but the foundations remain the same, such as perceptrons, backpropagation and activation functions. Bishop is not the most pedagogical author, especially if you read more than the first few chapters, so if you need someone to hold your hand while reading, this is probably not the best place to start. In any case, the book seems great as a reference and if you like this kind of stuff, you should definitely read it at some point.

4out of 5VW–A concepts-oriented textbook about Machine Learning, relatively detailed considering the breadth of topics it covers, and suitable for text-study. I would not recommend this book as the first to be introduced to Machine Learning, because it tends to go down rabbit holes of technical calculations, which makes things very concrete, but makes it difficult for the reader to keep track of what problem we're solving and to take a step back. I've found MacKay's Information Theory, Inference and Learning A concepts-oriented textbook about Machine Learning, relatively detailed considering the breadth of topics it covers, and suitable for text-study. I would not recommend this book as the first to be introduced to Machine Learning, because it tends to go down rabbit holes of technical calculations, which makes things very concrete, but makes it difficult for the reader to keep track of what problem we're solving and to take a step back. I've found MacKay's Information Theory, Inference and Learning Algorithms to be more insightful, and (surprisingly) Manning's Introduction to Information Retrieval to do a better job at motivating and illustrating ML problems and approaches from the ground up. To me, PRML really shines as a resource to go deeper after an introdution, with a technical exposition that is both detailed and general-purpose, and a wealth of exercises for self-study (highly appreciated!). It's especially relevant if you're interested in Bayesian approaches. It fits as a good stepping stone, right after conceptual introductions, and before more specialized material such as Deep Learning or Gaussian Processes for Machine Learning. One could probably position PRML as the Bayesian counterpart to The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Some specifics: 1. This is NOT a practical resource on ML, in particular it will not teach or demonstrate any software tool. 2. Contains many exercises, a good deal of them have available corrections, so it's suitable for self-study. 3. Does introduce Neural Networks, but won't go beyond the basic architectures. Does also introduce "classical" ML techniques such as linear models, SVMs, Gaussian Processes, etc. 4. The use of Graphical Models as a modeling tool for a broad range of situations is particularly insightful. 5. It's quite a long read - don't feel like you have to read all of it, it can fruitfally be used as reference material. The introduction chapter on its own is extremely insightful - to read and re-read.

4out of 5Oleg Dats–Read it if you want to really understand statistical learning. A fundamental book about fundamental things. It is not the easy one but it will pay off.

5out of 5Felipe–This book attempts to be self-contained, e.g: starting from probability and the Bayes' theorem as the foundation. But it is by no means an introductory book. If you have not developed an intuition for statistics and probability, you will find this book a very painful read. This being said, I think you might want to use other books in combination with this book as reference to make the process a little bit easier. In addition, some people have put together code (look for PRMLT on GitHub) in Matlab, This book attempts to be self-contained, e.g: starting from probability and the Bayes' theorem as the foundation. But it is by no means an introductory book. If you have not developed an intuition for statistics and probability, you will find this book a very painful read. This being said, I think you might want to use other books in combination with this book as reference to make the process a little bit easier. In addition, some people have put together code (look for PRMLT on GitHub) in Matlab, that help illustrating the concepts in terms of code that you can experiment with, and seeing some of the concepts of this book at work.

4out of 5Trung Nguyen–I consider PRML one of the classic machine learning text books despite its moderate age (only 10 years). The book presents the probabilistic approach to modelling, in particular Bayesian machine learning. The material seems quite intimidating for readers that come from a not-so-strong mathematical background. But once you get over the initial inertia and practice deriving the equations on your own, you'll get a deep understanding of the content. I consider PRML one of the classic machine learning text books despite its moderate age (only 10 years). The book presents the probabilistic approach to modelling, in particular Bayesian machine learning. The material seems quite intimidating for readers that come from a not-so-strong mathematical background. But once you get over the initial inertia and practice deriving the equations on your own, you'll get a deep understanding of the content.

4out of 5Nick–Very decent mathematical overview of Data Science/ML with an emphasis on variational methods. It is particularly good intro to Bayesian stats/philosophy with nice pictures which is a good for those who don't know stats that well but are scientists at heart. I enjoyed it but I also recommended it many times over to friends who knew far less stats than me and they often were extremely compelled by it (good for teaching). It is an intro book, just to note. Very decent mathematical overview of Data Science/ML with an emphasis on variational methods. It is particularly good intro to Bayesian stats/philosophy with nice pictures which is a good for those who don't know stats that well but are scientists at heart. I enjoyed it but I also recommended it many times over to friends who knew far less stats than me and they often were extremely compelled by it (good for teaching). It is an intro book, just to note.

5out of 5El–Slightly dense textbook (in terms of algebra, theory and also to read) and not very well structured in terms of concepts, best to be read alongside a taught course imo. Also narrow, only focuses on Bayesian approaches. However, very comprehensive on Bayesian ML and has some great, clear diagrams that really help learning.

5out of 5DJ–recommended reading on machine learning from Gatsby (the neuroscience group in London, not the fictional Roaring 20s tail-chaser)

5out of 5Miguel–Apply Bayesian reasoning to anything. Not for beginners but after reading 10 times it gets clearer ;). This was the book in my machine learning course and it was hard to process, but worth it.

5out of 5Mahdi shafiee–I'm not read whole of book but i believe this is book is one of best reference for machine learning. One of weak points is Deep learning not presented. I'm not read whole of book but i believe this is book is one of best reference for machine learning. One of weak points is Deep learning not presented.

5out of 5John–First off, it needs to be noted that there are things about this book that are old and should be ignored. Deep learning, and anything involving that, has went way beyond this. The neural network discussion is very old. Some of the approaches it discusses are also largely out of favor, as they've been supplanted by other technologies. But things sometimes come around again. Beyond that, though, there's a lot of good fundamentals that haven't changed so much. As other reviewers note, it is a heavi First off, it needs to be noted that there are things about this book that are old and should be ignored. Deep learning, and anything involving that, has went way beyond this. The neural network discussion is very old. Some of the approaches it discusses are also largely out of favor, as they've been supplanted by other technologies. But things sometimes come around again. Beyond that, though, there's a lot of good fundamentals that haven't changed so much. As other reviewers note, it is a heavily Bayesian approach, which is something I like. I read it a long time ago, was good then, still reads well.

4out of 5Chengchengzhao–I read this book during my graduate study. At that time, this book was just so good. There are so many details in it, I learned to derive the EM algorithm for Gaussian mixture models and used the knowledge to pass one interview for job hunting. However, this book is written by a world-renowned Bayesian machine learning expert. If you want to know some frequentist points of views about the ML area, this may not help. In short, this is a great book to read!

4out of 5Dhanya Jothimani–Actual Rating: 4.5 Recommended for understanding the Bayesian perspective of Machine Learning algorithms but it doesn't give a comparative analysis with Frequentist approach. Good for learning the (theoretical or ) mathematical aspects of algorithms and their graphical representation. Focus on real world applications missing. P.S.: Used for teaching Bayesian Statistics and Machine Learning course for graduate students Actual Rating: 4.5 Recommended for understanding the Bayesian perspective of Machine Learning algorithms but it doesn't give a comparative analysis with Frequentist approach. Good for learning the (theoretical or ) mathematical aspects of algorithms and their graphical representation. Focus on real world applications missing. P.S.: Used for teaching Bayesian Statistics and Machine Learning course for graduate students

5out of 5A Mig–Strong emphasis on the Bayesian viewpoint and heavy on equations. The coloured panels with the short bio of famous statisticians and other important scientific figures were a welcomed addition to make the whole thing more digest. So overall a difficult read, certainly not the easiest to learn all the basics but an excellent manual for the researcher looking for something specific, especially if Bayesian related.

5out of 5Kirill–If you want to learn about Bayesian Machine Learning this is The Book. However, it falls short on intuitive explanations compared to ISLR and ESLR, so those might be better for a first introduction to ML.

4out of 5Christopher Hendra–A really good read for graduate student intending to pursue data science/statistics/machine learning related research. It is comprehensive and provide the necessary amount of rigour to understand basic concepts beyond the intuition level

4out of 5Sten Sootla–A foundational book that covers the fundamentals of probabilistic pattern recognition. An essential text that widens the horizon of machine learning engineers beyond the discriminative deep learning models as we have today.

4out of 5Rodrigo Rivera–Even more than 10 years after its publication, this book remains the best learning source for bayesian machine learning. Clear explanations, colorful figures and a beautiful edition makes this book a truly classic. Hope one day Chris Bishop gives us a second edition.

4out of 5Kent Sibilev–One of the best textbooks on ML. My favorite topics of the books are Neural Networks, Graphical Methods, EM algorithm and one of the best introduction to Kernel Machines such as SVN and RVN. The book takes very strong emphasis to Bayesian inference.