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There are two textbooks that I most love and am most afraid of in the world: Introduction to Algorithms by Cormen et al. and Artificial Intelligence: A Modern Approach by Norvig et al. I have started the "AI: A Modern Approach" more than once, but the book is so dense and full of theory that I get discouraged after a couple of weeks and stop.

I am looking for a similar AI book but with an equal emphasis on theory and practice. Some examples of what I am looking for:

  • The Elements of Statistical Learning by Tibshirani et al. (detailed theory)

  • An Introduction to Statistical Learning: With Applications in R by Tibshirani et al. (theory+practical)

  • Digital Image Processing by Gonzalez et al. (detailed theory)
  • Digital Image Processing Using MATLAB by Gonzalez et al. (theory+practical)
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In addition to the books already mentioned, I would like to recommend to you some that helped me understand the basics and guided me through my first AI / CI implementations.

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Firstly, you should be clear about on which subject you want to study.

AIMA deals with conventional ai algorithms like path planning, logic etc.

Elements of statistical learning is a machine learning book which covers most of the machine learning algorithms you will come across (spare deep learning). digital image processing is an entirely different field which deals with processing images (contouring, reconcstruction, filters etc.)

If you want a clear cut ai book AIMA is hands down the best, but you cant read it one go, it is more like a referance textbook. instead it is much better to enroll in an online course and follow the book as a supplement. (check udacity)

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