 | | |
| 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
You Save: $26.20 (31%)
Buy New/Used from $58.75
Avg. Customer Rating:  (41 reviews)
Sales Rank: 16054
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
|
| Customer Reviews:
 recommend for non statistics majors May 9, 2007
35 out of 38 found this review helpful
I started to read this book after I gave up the book "element of statisitcal learning" which I read about 80 pages. I won't say that the latter book EoSL is bad, but it definitely assumes a much higher math background. Also it doesn't give all the derivations and reasonings, so it may take a long time to understand a single paragraph. The reading is slow and frustrating. I read each chapter twice, but still do not think I did get it in my heart.
By contrast, the book "Pattern Recognition and machine learning" assumes much less math background, and usually gives complete derivation and reasoning, which makes it a pleasure to read. Therefore, if you are not in statistics major (but a CS major with reasonable statistics background), I recommend you to start this book.
Answers to some problems are posted in the author's website (just google the author's name). It is a big plus to me.
Excellent Reference March 29, 2007
1 out of 2 found this review helpful
As a graduate student doing research in Computer Vision, I have found Bishop's book to be an excellent reference. I purchased the book to help myself pick up some important techniques that were never covered in my formal coursework. I certainly haven't read it in its entirety yet, but have read many sections and am impressed with the explanations given. The book covers a broad spectrum of topics (just what I wanted in that regard), some complicated, and does so in a pleasantly clear and intuitive manner. I also found the brief biographies on mathematicians I've heard of over the years very interesting. Overall, an excellent reference!
Excellent treatment of a difficult subject March 24, 2007
0 out of 1 found this review helpful
Bishop does an excellent job of conveying an intuitive understanding of a wide and complex range of topics. Where so many authors just present theorems and proofs, this book goes to the trouble of showing graphically what is going on with the various problems and techniques described. If you are among the target audience specified in the "Book Description" (advanced undergraduate upwards) you should be able to follow the notation; and you will not be disappointed to discover that "this is a textbook" because the description clearly states that it is!
 disappointed, cover a lot but few is explained enough March 4, 2007
9 out of 14 found this review helpful
Not easy for a student with no experience on Machine Learning before. It might be useful for those researchers who have seen a lot. Many "straightfoward" or "easy to show" questions are not easy for me at a first glance. Many discussions are left to numerous papers, which does not make problem more clear but more puzzled. Many many comments are made in a very high level without detailed explanation. Most exercises are only algebra and matrix theory, nothing to do with Algorithm. I have to read other books first.
Thorough but vastly unclear February 28, 2007
60 out of 68 found this review helpful
I can appreciate others who might think that this is a great book.... but I am a student using it and I have some very different opinions of it.
First, although Mr. Bishop is clearly an expert in Machine Learning, he is also obviously a HUGE fan of Bayesian Statistics. The title of the book is misleading as it makes no mention of Bayes at all but EVERY CHAPTER ends with how all of the chapter's contents are combined in a Bayes method. That's not bad it's just not clear from the title. The title should be appended with "... using Bayesian Methods"
Second, while it is certainly a textbook, the author clearly has an understanding of the material that seems to undermine his ability to explain it. Though there are mentions of examples there are, in fact, none. There are many graphics and tiny, trivial indicators, but I can't help to think that every single one of the concepts in the book would have benefited from even a single application. There aren't any. I am lead to believe that if you are already aware of many of the methods and techniques that this would be an excellent reference or refresher. As a student starting out I almost always have no idea what his intentions are.
To make matter worse, he occasionally uses symbols that are flat-out confusing. Why would you use PI for anything other than Pi or Product? He does. Why use little k, Capital K, and Greek Letter Kappa (a K!) in a series of explanations. He does. He even references articles that he has written... in 2008!!
Every chapter seems to be an exercise to see how many equations he can stuff in it. There are 300 in Chapter 2 alone. Over and over and over again I have the feeling that he is trying to TELL me how to ride a bicycle when it would have been so much easier to at least let me see the view from behind the handle bars with my feet on the pedals. Chapter five on Neural Nets, for example, is abysmally over-complicated. Would you hand someone a dictionary and ask them to write a poem? ("Hey, all the words you need are in here!") Of course not.
Third, the book mentions that there is a lot of information available on the web site. The only info available on his website is a brief overview of the text, a detailed overview of the text (that's not a typo.... he has both), an example chapter, links to where the book can be purchased, and (actually, quite useful for creating slides) an archive of all of the figures available in the book. There are no answers to problems or explorations of any part of the material. The upcoming book might be amazing and exactly what I am looking for but it could be months away and another $50 or so to purchase it. Hardly ideal. How about putting some of that MatLab code on your site? *Something* to crystalize the concepts!
Finally, while the intro indicates this might be a good book for Computer Scientists it would actually make more sense to call it a Math book. More specifically a Statistics book. There are no methods, no algorithms, no bits of pseudo-code, and (again) no applications are in the text. Even examples that actually used hard numbers and/or elements from a real problem and explained would be much appreciated.
Maybe I am being a little critical and perhaps I want for too much but in my mind if you are writing a book with the goal of TEACHING a subject, it would be in your interest to make things clear and illustrative. Instead, the book feels more like a combination of "I am smart. Just read this!" and a reference text.
|
|
|
Powered by Associate-O-Matic
| | |