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Pattern Recognition and Machine Learning

4.24  ·  Rating details ·  930 Ratings  ·  24 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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Nate
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.
Manny
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
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.
Oldrich
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.
David
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.
Kjn
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.
Kirill
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.
Hannah
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.
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.
Michiel
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!
Darin
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.
Huyen
Dec 20, 2012 rated it it was ok
intolerably dry
DJ
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)
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.
Tim Josling
Dec 22, 2015 rated it it was amazing
Hard work but terrific.
Kirsten Frank
Jul 29, 2013 rated it liked it
Shelves: science
Slow and steady.
Хотло Ширнууд
Aug 09, 2014 rated it it was amazing
Excellent book with very detailed explanations on the fundamentals of the statistics and machine learning.
Jonathan
Mar 08, 2008 rated it it was ok
Shelves: research
Impenetrable! Mitchell is so much less painful.
Todd Johnson
Mar 18, 2007 rated it really liked it
Shelves: machine-learning
Covers a very wide range of material. I am told his book on Neural Nets (something of a classic) is somewhat better. Surprisingly little to say on a couple of interesting topics, such as Bayes error.
John Davis
Feb 15, 2015 marked it as partly-read
Dense but useful. Wish I hadn't sold my copy.
Miguel
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.
Kumar Deep
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Dec 12, 2016
Thành Ka
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Sep 16, 2014
Yaho Zhong
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Jan 24, 2014
Emre Velipasaoglu
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Nov 18, 2015
Alireza Nouri
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Jun 20, 2014
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