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Pattern Recognition and Machine Learning (Information Science and Statistics)
by Christopher M. Bishop
from Springer

Pattern Recognition and Machine Learning (Information Science and Statistics)

 

List Price: $84.95
Price: $65.50
You save: $19.45 (22%)

Media: Hardcover
Availability: Usually ships in 24 hours



Buy from: Canada France United Kingdom


Customer Reviews:

  • Avg. Customer Rating: 4.0 / 5.0

  • Probably the best book for machine learning
    I am a PhD student in machine learning. Bishop is really gifted and he explains very well basic and advanced concepts of machine learning. I would say that this book is much more comprehensive than Hastie's Statistical learning book The Elements of Statistical Learning. Very good illustrations and very complete. I would definitely recommend it for those who want to learn statistical/machine learning on their own

  • concentrates too much on the easy stuff
    The book is worth a look, but after some of 5 star reviews i read here, it was quite a disappointment. Yes, the book covers a lot of ground. Yes, the book has lots of nice pictures and easy examples, but that is exactly the problem. There are lots and lots of simple examples to explain the most basic concepts, but when it gets complicated the book often sounds as if the text was taken out of a mathematics book. For example: the basics of probability theory are introduced for over 5 pages with the example of... more info

  • Authorative text
    I am a PhD student who wanted to own a good book on pattern recognition. I asked my professor, who had recently attended an international conference on speech recognition, which book to buy. He said that several top academics in the field at the conference had agreed that this was THE book to have, and he agrees with them.
    After reading though the first few chapters I am impressed by the structured way concepts are related. I like that the basic probability theory needed to understand the concepts are... more info

  • Awesome
    Start right from the first page. No gimmicks. Plain old mathematics and useful stuff, then to machine learning. You always know, the rationale behind the chapters or the sentence. Very inspiring.


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