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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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    $\begingroup$ As nbro mentioned in his answer, Russell and Norvig is relatively broad, because it is meant as an introduction to all of AI, rather than an introduction to specific subfields (such as statistical/machine learning, etc.). $\endgroup$ Jan 16 at 23:53
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The following 2 books helped me understand the basics and guided me through my first AI / CI implementations.

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Why is AIMA dense?

Artificial intelligence is a broad field: that's why Artificial Intelligence: A Modern Approach (AIMA) may look a bit dense to newcomers, given that it covers many different aspects of AI, such as search, machine learning, and natural language processing.


AI is not just ML!

The first book in this answer is a good book, but it focuses on evolutionary computation approaches, which are often considered part of AI too. The other books that you mention in your post also focus on subfields of AI, such as machine learning or image processing, so they do not cover all aspects of AI.


Alternatives to AIMA

Artificial intelligence (by Patrick Winston)

If you want a good book similar to AIMA, you should definitely take a look at Artificial intelligence (3rd edition, 1992) by Patrick Winston, who was a professor at MIT and also director of the AI Lab at MIT. You can also find his free course on Artificial Intelligence (which I highly recommend) here.

Artificial Intelligence: A New Synthesis (by Nils J. Nilsson)

Another similar book that attempts to give an extensive overview of the AI field is Artificial Intelligence: A New Synthesis (1998) by Nils J. Nilsson (here is the link to Google Books, which shows you a preview of many pages of the book).

(Nilsson also wrote other important books related to AI and the philosophy of AI, such as The Quest for Artificial Intelligence: A History of Ideas and Achievements (2009), among other important contributions to the AI field, such as the robot Shakey and STRIPS.)

Artificial Intelligence: Structures and Strategies for Complex Problem Solving (by George Lugar)

There's also Artificial Intelligence: Structures and Strategies for Complex Problem Solving (6th edition, 2009) by George Lugar. This book is mentioned by Ben Goertzel in his paper Artificial General Intelligence: Concept, State of the Art, and Future Prospects as a "popular AI textbook". I've quickly skimmed through the table of contents and it really seems to provide an extensive overview of the AI field and its techniques.

Artificial Intelligence: Foundations of Computational Agents (by Poole and Mackworth)

Another book (that I consulted in the past), which covers many different sub-fields or topics of AI, such as machine learning, search, planning, reasoning, and knowledge-based systems, is Artificial Intelligence: Foundations of Computational Agents (2nd Edition) by David L. Poole and Alan K. Mackworth.

Artificial Intelligence: A Guide to Intelligent Systems (by Michael Negnevitsky)

There's also Artificial Intelligence: A Guide to Intelligent Systems (2nd edition, 2005) by Michael Negnevitsky, which covers expert systems, fuzzy systems, artificial neural networks, evolutionary computation, and hybrid systems.

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