Ebook Download Pattern Recognition and Machine Learning (Information Science and Statistics), by Christopher Bishop
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Pattern Recognition and Machine Learning (Information Science and Statistics), by Christopher Bishop
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This is the first textbook on pattern recognition to present the Bayesian viewpoint. The book presents approximate inference algorithms that permit fast approximate answers in situations where exact answers are not feasible. It uses graphical models to describe probability distributions when no other books apply graphical models to machine learning. No previous knowledge of pattern recognition or machine learning concepts is assumed. Familiarity with multivariate calculus and basic linear algebra is required, and some experience in the use of probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory.
- Sales Rank: #8536 in Books
- Published on: 2007-10-01
- Original language: English
- Number of items: 1
- Dimensions: 9.25" h x 7.25" w x 1.75" l, 3.90 pounds
- Binding: Hardcover
- 738 pages
Most helpful customer reviews
57 of 64 people found the following review helpful.
The book should change its title
By John E
This book (PRML) should be re-titled as "PRML: a bayesian approach". Yes, bayesian approach is very useful for machine learning, and sometimes the final goal of learning is to maximize some sort of posterior probability. However, if the author is such a huge fun of bayes statistics, please tell perspective readers in a clear way. Emphasize bayes aspects too much really hurt the quality of this book as a general-purpose textbook of machine learning.
For a better textbook of machine learning, I recommend:
1) The elements of statistical learning (perhaps this book a little hard for beginner in this field -- but as least better than PRML -- you can compare their chapters about linear regression to see which one is better).
2) Pattern classification (focus on classification, not regression. Also not very easy -- anyway, machine learning is not an easy field ^_^).
3) Machine Learning (a little old, but great for beginner.)
These three book also mention bayesian statistics, but in a proper way. If you have some experience in machine learning and have engineering-level math background, just choose the 1) or 2). If you are completely a beginner, first take a glance on 3), and then go to 1) or 2).
Finally, if you want a book that discusses machine learning purely from bayesian perspective, PRML is good.
195 of 205 people found the following review helpful.
Great Insights, but a hard read
By Sidhant
This new book by Chris Bishop covers most areas of pattern recognition quite exhaustively. The author is an expert, this is evidenced by the excellent insights he gives into the complex math behind the machine learning algorithms. I have worked for quite some time with neural networks and have had coursework in linear algebra, probability and regression analysis, and found some of the stuff in the book quite illuminating.
But that said, I must point out that the book is very math heavy. Inspite of my considerable background in the area of neural networks and statistics, I still was struggling with the equations. This is certainly not the book that can teach one things from the ground up, and thats why I would give it only 3 stars. I am new to kernels, and I am finding the relevant chapters difficult and confusing. This book wont be very useful if all you want to do is write machine learning code. The intended audience for this book I guess are PhD students/researchers who are working with the math related aspects of machine learning. Undergraduates or people with little exposure to machine learning will have a hard time with this book. But that said, time spent in struggling with the contents of this book will certainly pay-off, not instantly though.
27 of 31 people found the following review helpful.
Cannot keep it away!
By K. Pasad
For math heavy fields there are a usually a ton of books but 1 or 2 stand out in terms of their ability to tell a story, using math. Bishops book ranks among those selected few. A context: I read this book after covering some topics from Hatie et al. I am a EE major and occasionally use variant of this stuff in my daily work for signal processing.
IMHO the following make this book so readable as well very useful:
1. Consistent use of a small vocabulary and a few central ideas: all techniques are boiled down to basic fundamental ideas. The ideas are developed early on, very clearly and we are told early on that the rest of the book will grow on these ideas. In bishops case, in chapter one and two, he lays down the fundamentals of Maximum likelihood and Bayesian models, linear models, explains inference and decision, and builds upon these few principles.
2. Usage of terminology is consistent and no surprising new terminology or ideas are added anywhere.
3. The basic ideas are explained, again, every time they are used. Yes, it takes up a few additional lines and makes the material a bit redundant but it serves to reinforce the basic ideas on which everything is built. You do not scamper around in endless loops. Everything is right there-clear. You do not need Google.
4. Clearly and often illustrates how the big picture is composed of basic ideas and how the basic ideas manifest themselves in advanced topics.
5. Does the dirty work of solving the math. And does it in a clean way, without using excessive prefixes and greek letters. The little details matter, and IMHO that's what makes the book readable.
Master the chapter 2 and you will not be scared of advanced topics
My thougths on some negative comments:
1. The book is math heavy: No- the required math needed is covered in chapter 2. Everything revolves around it. Suck it up. ML is math.
2. Not enough intuition: There is. A lot of it, but in its own way. You need to master some of the basic math concepts (book covers it). Sorry.
3. Two much basic stuff repeated- That's what make the book so useful, continuous reinforcement.
4. Too much theory, not enough practice: Ya, there isn't any python code. But a practical text is for advanced user. For beginners, and intermediate, you are better off understanding the fundamentals, else, you will fall into the common trap of trying 5 different models on your data and averaging them. If you want code, just go to sklearn.
5. Bayesian heavy- True, but an understanding of Bayesian model help you understand what to strive for even if you don't use it.
I would recommend reading Hatie et al. after reading this book. Hastie's book is a very practical book. IMHO, you cannot choose between the two-each solves a different problem. Bishops develops the basics and Hastie takes it to practice. Spend time, read both, and don't fear the math!
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