Pattern Recognition and Machine Learning (Information Science and Statistics) |  | Author: Christopher M. Bishop
Publisher: Springer
List Price: $89.95
Buy New: $52.78
as of 11/20/2009 22:46 CST details
You Save: $37.17 (41%)
New (42) Used (18) from $52.78
Seller: oddesseyy
Rating:  48 reviews
Sales Rank: 16305
Media: Hardcover
Edition: 1
Pages: 738
Number Of Items: 1
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
| |
| Also Available In:
|
| Accessories:
|
| Similar Items:
| |
| Editorial Reviews:
Product Description
The dramatic growth in practical applications for machine learning over the last ten years has been accompanied by many important developments in the underlying algorithms and techniques. For example, Bayesian methods have grown from a specialist niche to become mainstream, while graphical models have emerged as a general framework for describing and applying probabilistic techniques. The practical applicability of Bayesian methods has been greatly enhanced by the development of a range of approximate inference algorithms such as variational Bayes and expectation propagation, while new models based on kernels have had a significant impact on both algorithms and applications. This completely new textbook reflects these recent developments while providing a comprehensive introduction to the fields of pattern recognition and machine learning. It is aimed at advanced undergraduates or first-year PhD students, as well as researchers and practitioners. 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. The book is suitable for courses on machine learning, statistics, computer science, signal processing, computer vision, data mining, and bioinformatics. Extensive support is provided for course instructors, including more than 400 exercises, graded according to difficulty. Example solutions for a subset of the exercises are available from the book web site, while solutions for the remainder can be obtained by instructors from the publisher. The book is supported by a great deal of additional material, and the reader is encouraged to visit the book web site for the latest information. Coming soon: *For students, worked solutions to a subset of exercises available on a public web site (for exercises marked "www" in the text) *For instructors, worked solutions to remaining exercises from the Springer web site *Lecture slides to accompany each chapter *Data sets available for download
|
| Customer Reviews:
Showing reviews 1-5 of 48
A good reference. November 20, 2009
Almon D. Ing (Austin, TX)
This is a great reference for machine learning. There are many figures and the treatment is clear and comprehensive.
To my knowledge, it is the best treatment available. It's not a perfect treatment, but at this stage in the field's development, there is no such thing. Very good for a 2001 book.
 Great book November 8, 2009
N. Djuric (Philly, PA)
0 out of 1 found this review helpful
This is really great book if your are interested in Machine Learning or Data Mining. Although some of the chapters are written on really high level, it will definitely help u if you're graduate student.
Nice even for fair beginners October 17, 2009
Vladislavs Dovgalecs (Bordeaux, France)
First of all the reading gives overall positive impression both about the quality of material and the illustrations. Author explains pretty well the basic aspects and the text is easy to follow even for those not very confident with mathematics. As author claims, everything seems to revolve around Bayesian approach but this is his choice. I would recommend the book for graduate students doing their work in machine learning domain.
 Don't buy this if you don't already know everything about the field October 15, 2009
shanusmagnus (Elk River, MN United States)
3 out of 3 found this review helpful
It's hard to figure out who would actually benefit from this book - it amounts to seven hundred pages of equations interrupted by blocks of text that fail to provide any intuition whatever for the techniques they are describing, and the occasional graph which is remarkable in the universe of graphs as being scarcely more informative than the equations it is meant to illustrate.
Seriously, you have to wonder wtf Bishop thought he was doing here. As a catalog of equations for people who already thoroughly understand the learning algorithms I suppose the book can be considered adequate. For any didactic purpose you're wasting your time - you can find dense, technically correct but incomprehensible descriptions for any of these methods online, for free. A textbook ought to aspire to more - should bring some order to the chaos, re-tell a technical story in a new light to make it more sensible and intuitive. This book is so bad in these regards that it makes me angry.
On a related note, I can't believe that Duda and Hart is still the best machine learning / pattern rec. book on the market after thirty years or whatever. This field is dying for a book by someone with even an INKLING of how to teach, or at least willing to make an effort to try.
good book but maybe a little to thick October 9, 2009
Dan (Omaha, NE)
Christopher Bishop really knows his stuff and it is obvious by using his book. I just wish he would follow up all of his math with a clear algorithm for implementing the techniques presented in his book.
Showing reviews 1-5 of 48
|
|
|
|
