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I have been told that as part of my degree year I will be learning about Artificial Intelligence. My lecturers have recommended a couple of books:

Artificial Intelligence: A Systems Approach (Computer Science)
Artificial Intelligence (A Modern Approach)

I've watched a couple of lectures hosted by Jeff Hawkins, they were good although a little advanced for me. Could someone recommend any other books or lectures that introduce the topic?

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Can't recommend any specific books or anything, but I would suggest looking into probability. Some aspects of AI use it a lot, and it takes a little while to get your head round it if you're not already familiar with it. –  Flynn1179 Jun 6 '11 at 13:48
possible duplicate of As a novice which book should I buy about Artificial Intelligence? –  Gilles Jun 6 '11 at 15:23
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migrated from stackoverflow.com Jun 6 '11 at 15:23

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3 Answers

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This is the book (Artificial Intelligence: A Modern Approach) I used in College ... http://aima.cs.berkeley.edu/

I remember it being a good book and it's recommended by a lot of people.

For Machine Learning - i would recommend the following book: http://www.cs.cmu.edu/~tom/mlbook.html (Machine Learning by Tom Mitchell).

again, it's the book i used in college and it gives a good overview of ML concepts.

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MIT OpenCourseware is often very good (and free):

6.034 Artificial Intelligence:

This course introduces representations, techniques, and architectures used to build applied systems and to account for intelligence from a computational point of view. This course also explores applications of rule chaining, heuristic search, logic, constraint propagation, constrained search, and other problem-solving paradigms. In addition, it covers applications of decision trees, neural nets, SVMs and other learning paradigms.

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The course is free, yes, but the text is certainly not (Amazon sells it for over $100). Still, the assignments, additional reading, and exams are nice. –  Corbin March Jun 6 '11 at 21:41
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The above-mentioned are good, just to throw a few other ones that I think are good into the mix:

For genetic, rule-based, and some simple neural-net material: Artificial Intelligence: A Guide to Intelligent Systems by Michael Negnevitsky (used in my AI class).

For ML concepts including in-depth neural-net, classification, supervised, unsupervised, and semi-supervised learning, as well as broad range of statistics/math: Machine Learning: An Algorithmic Perspective by Stephen Marsland (used in my ML class)

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