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Pattern Recognition and Machine Learning

4.28  ·  Rating details ·  1,255 ratings  ·  37 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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4.28  · 
Rating details
 ·  1,255 ratings  ·  37 reviews

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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 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.
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.
Jul 15, 2010 added it
Shelves: math
recommended reading on machine learning from Gatsby (the neuroscience group in London, not the fictional Roaring 20s tail-chaser)
Van Huy
Feb 19, 2018 rated it really liked it
Took me a year to finish this book :D
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.
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,
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!
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.
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
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.
Jan 05, 2017 rated it it was amazing
A very classic book of Machine Learning. Always strongly recommend to those who want to get into the field of ML.
Dec 28, 2017 rated it it was amazing
This is a great book for anyone starting on machine learning. Great details on the mathematics behind supervised and unsupervised learning.
Muhammed Cinsdikici
Jun 23, 2018 rated it it was amazing
Shelves: machine-learning
This is the fundamental textbook in Machine Learning area. Bishop explains the domain clearly.
Thirumal Alagu
Dec 21, 2018 rated it it was amazing
I got this book recommended by Microsoft, topics coved in this book is good to understand.
Michael Kareev
Jul 19, 2018 rated it really liked it
Extremely dense and complicated book that can take months to read.
It overlaps a lot with "The Elements of Statistical Learning" but the latter is more user-friendly.
Madhur Ahuja
Nov 26, 2018 rated it it was amazing
Shelves: tech
Fantastic book. This is available for free now
Ritwik Gupta
Jan 09, 2018 rated it it was amazing
Without a doubt the most comprehensive ML book I have read in my life. Extremely math heavy, but chances are that you're reading this book because of that reason.
Oct 15, 2018 rated it it was amazing
Shelves: cs
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.
Daniel Korzekwa
Nov 09, 2011 rated it really liked it
Very good book on probabilistic approach to machine learning. It goes from the elementary building blocks of probability distributions, up to the higher level frameworks of Bayesian Networks and Factor Graphs. The best book I've read so far on Bayesian Networks in a continuous and hybrid space. It's quite heavy on math, especially linear algebra and matrix manipulations.
Oct 09, 2010 rated it really liked it
Very good reference for machine learning and data mining.
I found it somewhat technical and abstract in times (there are no real life examples), some concepts can be explained a bit more intuitively.
Ariel Krieger
Oct 14, 2014 rated it really liked it
This is the definitive bible of ML and PR. It builds gradually and is well written.
Have no doubt, this fucker is HARD. If you don't know math (at least at an undergrad level) don't bother.

Machines will never learn!
This book covers the standard topics in pattern recognition, but I prefer the Duda book. What machine learning is in the book is related to pattern recognition, so this really isn't a general machine learning book like Machine Learning by Mitchell.
Jin Shusong
Mar 07, 2016 rated it it was amazing
Very good introduction to machine learning.
Dec 20, 2012 rated it liked it
intolerably dry
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  • Pattern Classification
  • The Elements of Statistical Learning: Data Mining, Inference, and Prediction
  • Data Mining: Practical Machine Learning Tools and Techniques
  • Machine Learning
  • Information Theory, Inference and Learning Algorithms
  • Machine Learning: A Probabilistic Perspective
  • Probabilistic Graphical Models: Principles and Techniques
  • Machine Learning: An Algorithmic Perspective
  • Bayesian Reasoning and Machine Learning
  • Bayesian Data Analysis
  • Machine Learning for Hackers
  • Programming Collective Intelligence: Building Smart Web 2.0 Applications
  • Mining of Massive Datasets
  • Machine Learning in Action
  • Introduction to Information Retrieval
  • Statistical Inference
  • Paradigms of Artificial Intelligence Programming: Case Studies in Common LISP
  • An Introduction to Probability Theory and Its Applications, Volume 1

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