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Pattern Recognition and Machine Learning

4.29  ·  Rating details ·  1,521 ratings  ·  61 reviews
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 ...more
Hardcover, 738 pages
Published April 6th 2011 by Springer (first published 2006)
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Manuel Antão
Apr 22, 2019 rated it really liked it
Shelves: 2019
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
Jun 29, 2008 rated it liked it
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.
Mar 09, 2010 is currently reading it
Dave, who knows about these things, recommended it... I have just ordered a copy.
Wooi 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 ...more
Aug 13, 2012 rated it liked it
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.
Oct 15, 2018 rated it really liked it
Shelves: cs, technical-10x
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.
Aasem Bakhshi
May 26, 2020 rated it really liked it
An amazing textbook that would never get old.
Oct 04, 2013 rated it it was ok
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.
Aug 13, 2007 rated it really liked it
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.
Van Huy
Feb 19, 2018 rated it really liked it
Took me a year to finish this book :D
Fernando Flores
Mar 22, 2019 rated it really liked it
Shelves: it
One of my first book on machine learning, this book can be painful if you don't have a solid background in algebra.
Emil Petersen
Aug 12, 2020 rated it really liked it
Shelves: computer-science
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 ...more
Mar 27, 2020 rated it really liked it
Shelves: science
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
Oleg Dats
May 20, 2019 rated it it was amazing
Shelves: ai
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.
Ibrahim Sharaf ElDen
Jul 24, 2019 rated it really liked it
Shelves: read-tech
Focusing too much on the Bayesian approach, can be very hard if your mathematics (esp. probabilities) foundations are not that solid. Doesn't recommend it for people who are looking to start in machine learning, or learn about it from the practical side, the book is very theoretical.
Nov 25, 2017 rated it liked it
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,
Trung Nguyen
Jul 11, 2015 rated it it was amazing
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.
Jan 25, 2018 rated it it was amazing
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.
Jan 18, 2020 rated it liked it
Shelves: reference
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.
Jul 15, 2010 added it
recommended reading on machine learning from Gatsby (the neuroscience group in London, not the fictional Roaring 20s tail-chaser)
Feb 03, 2013 rated it really liked it
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.
Mahdi shafiee
Jan 26, 2017 rated it really liked it
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.
May 20, 2020 rated it really liked it
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
Feb 09, 2019 rated it really liked it
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!
Dhanya Jothimani
Mar 30, 2019 rated it really liked it
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
A Mig
Apr 19, 2019 rated it liked it
Shelves: science-tech
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.
Jul 30, 2017 rated it really liked it
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.
Christopher Hendra
Aug 21, 2018 rated it it was amazing
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
Sten Sootla
Nov 18, 2018 rated it it was amazing
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.
Rodrigo Rivera
May 09, 2019 rated it it was amazing
Shelves: machine-learning
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.
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