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Reading Guide To AI Design & Neural Networks?

Posted by kdawson on from the open-the-library-doors-hal dept.

Raistlin84 writes "I'm a PhD student in theoretical physics who's recently gotten quite interested in AI design. During my high school days, I spent most of my spare time coding various stuff, so I have a good working knowledge of some application programming languages (C/C++, Pascal/Delphi, Assembler) and how a computer works internally. Recently, I was given the book On Intelligence, where Jeff Hawkins describes numerous interesting ideas on how one would actually design a brain. As I have no formal background in computer science, I would like to broaden my knowledge in the direction of neural networks, pattern recognition, etc., but don't really know where to start reading. Due to my background, I figure that the 'abstract' theory would be mostly suited for me, so I would like to ask for a few book suggestions or other directions."

3 of 266 comments (clear)

  1. PDP by kahizonaki · · Score: 5, Informative

    Parallel Distributed Processing (both books) by Rumelhart, McClelland, and the PDP research group, 1986. "THE" classic neural network resource--and still somewhat relevant.

  2. AIMA by omuls+are+tasty · · Score: 5, Informative

    Artificial Intelligence: A Modern Approach by Rusell and Norvig is more or less the standard AI textbook and the book I'd suggest to get an overview of AI and its different methodologies. Mind you, it's over 1000 pages, but a very interesting read.

  3. machine learning resources by Anonymous Coward · · Score: 4, Informative

    Following Books are must have for machine learning enthusiasts:

    Christopher Bishop
    http://research.microsoft.com/~cmbishop/prml/

    Richard Duda
    http://rii.ricoh.com/~stork/DHS.html

    There you will get an insight how machine learning methods (like neural networks, SVM, boosting, bayes classificator) work

    for general AI (not so much in direction of statistical learning as the books above, but more into higher level learning like inference rules) I can recommend published work done by

    Drew McDermott
    http://cs-www.cs.yale.edu/homes/dvm/