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| Pattern Recognition and Machine Learning (Information Science and Statistics) | 
 enlarge | Author: Christopher M. Bishop
Publisher: Springer
Category: Book
List Price: $84.95
Buy New: $58.75
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Avg. Customer Rating:  (41 reviews)
Sales Rank: 16261
Languages: English (Original Language), English (Unknown), English (Published)
Media: Hardcover
Edition: 1
Number Of Items: 1
Pages: 738
Shipping Weight (lbs): 4
Dimensions (in): 9.4 x 7.6 x 1.8
ISBN: 0387310738
Dewey Decimal Number: 006.4
EAN: 9780387310732
ASIN: 0387310738
Publication Date: October 1, 2007
Availability: Usually ships in 1-2 business days
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| Customer Reviews:
 Great Insights, but a hard read June 16, 2007
14 out of 16 found this review helpful
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.
Another book about machine learning without a clear theoretical backbone. June 2, 2007
28 out of 39 found this review helpful
Bishop's book about machine learing and pattern recognition is well written and the figures are really pretty because they are in color and informative. Overall the book looks very nice and it is fun to read in. In my opinion only the book 'The Elements of Statistical Learning' by Hastie et al. looks comparably well.
The book is a textbook rather than a monograph and, hence, intended for students rather than researchers and the coverage of machine learning topics is thorough without being able to cover every topic in deepth. This is not really a draw back because no book is able to do this anyway. The presentation of the methods is informative and, depending on the background of the reader, clear enough to figure out how it works to use the method.
What is the problem: I do not like that the methods are introduced not rigourously but by examples. That mean Bishop does not have the definiton, theorem, proof style but is more heuristic. This may sound very helpful for the reader not familiar with the topic to reduce the barrier of understanding by providing examples to visulalize the problem. The problem is, in my opinion, that this is not the case but the oposite. In think it is never wrong to provide examples and it is absolutely desirable but after the examples are given and one has an intuitive understanding of the problem one wants to see its formal solution because that's what machine learning is about, it is applied statistics. For this reason I give only 4 instead of 5 points (but not less because also all the other books about this topic fail in this respect).
Overall, the book is well done and certainly a good source of information for students and researches.
 A delight to read! May 25, 2007
0 out of 3 found this review helpful
This book is a delight to read for those interested in pattern recognition and machine learning. It presents in a clear and elegant way the fundamental ideas of these fast moving research fields. For example, the chapter on Graphical Models introduces sophisticated algorithms incrementally with a good balance of illustrations on small examples and general case discussions. This book is an excellent reference book for PR/ML researchers, PhD students and the more advanced undergraduate students.
Excellent book! May 15, 2007
1 out of 3 found this review helpful
Great book!. I recommend it to anyone who wants to learn Machine Learning. The book it's very easy to read. The author starts every topic with very intuitive examples before going into more complex formulations.
Good text book! May 12, 2007
1 out of 5 found this review helpful
This book friendly explains all, or almost all, the importants issues in the fild of Machine Learning.
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