- Paperback: 120 pages
- Publisher: Prentice Hall; 3 edition (7 Aug 2009)
- ISBN-10: 0136022928
- ISBN-13: 978-0136022923
- Average Customer Review: 4.0 out of 5 stars See all reviews (7 customer reviews)
- See Complete Table of Contents
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Customer Reviews
Most Helpful Customer Reviews
14 of 14 people found the following review helpful
Format:Paperback
I bought this book as I wanted an overview on AI. Well, this is even more than that. It's a great book, very easy to understand. Every chapter starts with the simple basic definitions, and gets more complicated, and there is a generous amount of information and pages for each topic. Reading it I find the answers to questions I've had for a while, so I really love how helpful this book can be. I have the third edition, and I am mainly interested in the probabilistic reasoning part. The authors have a great writing style, they have such good jokes that it really gives me the feeling they loved writing the book and they want the reader to be relaxed and enjoy the field, so it's hard to put it down once I open it. I think it also has a good flow, the reading flow and the ideas are not interrupted, and it progresses nicely, without making me feel overloaded with information.
The mathematics is understandable, I didn't find pages with more formulas than text as I saw in other books in the field. So it's not a scary sight.
I think that compared to any other book on AI this is way beyond, and it is the one to buy as it saves a lot of time of searching the web for answers. So if you need a book on AI, if this book cannot help you, no other book will. And another aspect I really appreciate at this book is its price. I can't believe when I read it that it was the cheapest among my latest acquisitions on ai and machine learning, and it contains so much more useful information than any of them! This is a great thing. You will notice that the paper quality is not as high as in other books, but this is something I love. I don't want to pay for the glossy paper, as I'd rather pay for more pages!
Just another point for who might be interested in case based reasoning : this book almost doesn't mention it! It's only mentioned in a Bibliographical and Historical chapter, in one paragraph. Therefore, I'd advise to check the table of contents and see if the authors write about what you're interested in. If they do, that'll be a pleasure to read!
The mathematics is understandable, I didn't find pages with more formulas than text as I saw in other books in the field. So it's not a scary sight.
I think that compared to any other book on AI this is way beyond, and it is the one to buy as it saves a lot of time of searching the web for answers. So if you need a book on AI, if this book cannot help you, no other book will. And another aspect I really appreciate at this book is its price. I can't believe when I read it that it was the cheapest among my latest acquisitions on ai and machine learning, and it contains so much more useful information than any of them! This is a great thing. You will notice that the paper quality is not as high as in other books, but this is something I love. I don't want to pay for the glossy paper, as I'd rather pay for more pages!
Just another point for who might be interested in case based reasoning : this book almost doesn't mention it! It's only mentioned in a Bibliographical and Historical chapter, in one paragraph. Therefore, I'd advise to check the table of contents and see if the authors write about what you're interested in. If they do, that'll be a pleasure to read!
13 of 13 people found the following review helpful
Format:Paperback
This book is over 1000 pages and it has the most wild and widely used algorithms. It focuses on explaining the reader how the algorithms work and why they work rather then focusing on their implementation (and when they implement algorithms they don't do it the best way).
It has many algorithms from the most diverse areas, but the pseudo-code implementations are weak and way too general to be of any use, further more, their evaluation function is also not recommendable in most cases.
Other than that, it is a fluid book with hundreds of illustrations and schemes that will help any reader understand how things should work easily in most cases.
It has many algorithms from the most diverse areas, but the pseudo-code implementations are weak and way too general to be of any use, further more, their evaluation function is also not recommendable in most cases.
Other than that, it is a fluid book with hundreds of illustrations and schemes that will help any reader understand how things should work easily in most cases.
3 of 3 people found the following review helpful
Format:Paperback
This 1100 page tome is the class textbook for the AI course run at Stanford University.
It is up to date and gives a lot of information about new approaches to AI and machine learning that have been developed in the last 10 years.
However it is not a light read. There is an assumption that you are already familiar with the mathematics around probabilities, linear algegra and matrix manipulation, as well as being able to recognise a wealth of greek symbols used in the sometimes confusing notations used. There are a few pages about these mathematical topics in an appendix bit you will probably need to brush up with other material referenced outside of the book if you want to fully understand or try to apply the probabalistic learning techniques described.
If you just want to know what AI is about then there are a lot of easier introductory books around, but if you want to know in detail the sort of probabalistic learning techniques that allowed the Stanford University team to win the $2million prize for the DARPA autonomous vehicle challenge then this book is one of the few around that covers the material in depth.
It is up to date and gives a lot of information about new approaches to AI and machine learning that have been developed in the last 10 years.
However it is not a light read. There is an assumption that you are already familiar with the mathematics around probabilities, linear algegra and matrix manipulation, as well as being able to recognise a wealth of greek symbols used in the sometimes confusing notations used. There are a few pages about these mathematical topics in an appendix bit you will probably need to brush up with other material referenced outside of the book if you want to fully understand or try to apply the probabalistic learning techniques described.
If you just want to know what AI is about then there are a lot of easier introductory books around, but if you want to know in detail the sort of probabalistic learning techniques that allowed the Stanford University team to win the $2million prize for the DARPA autonomous vehicle challenge then this book is one of the few around that covers the material in depth.
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Most Recent Customer Reviews
Brilliant book
A really good book, written in a way that people can easily get an overview before going into details, which is extremely good for understanding, not to mention the pseudo code... Read more
Published 1 month ago by Dr. Yu Su
Very satisfied.
Very good textbook. Gives enough examples too help you fully understand the concepts without becoming tiresome. Read more
Published 1 month ago by Aris
Excellent
This is a very complete work on AI. Theory and practice are perfectly balanced.
Very good book and excellent delivery!
Very good book and excellent delivery!
Published 10 months ago by Fran
Hard to read
I don't think this book is for beginner. It is really hard to read, I don't like his writing method, I even stuck in the first page. Read more
Published 14 months ago by Mr. Lin Cao
Listmania!
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