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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.). $\endgroup$
    – The Pointer
    Commented Jan 16, 2021 at 23:53

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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 computational intelligence 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

Title Author(s) Year Topics Comments
Artificial intelligence Patrick Winston 1992 (3rd edition) Search, rule-based systems, machine learning, evolutionary algorithms, etc. I occasionally consulted this book; Patrick Winston 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 Nils J. Nilsson 1998 Neural networks, evolutionary algorithms, search, knowledge representation and reasoning, planning, Bayesian networks, etc. 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: A Guide to Intelligent Systems Michael Negnevitsky 2005 (2nd edition) Expert systems, fuzzy systems, artificial neural networks, evolutionary computation, hybrid systems, etc.
Artificial Intelligence: Structures and Strategies for Complex Problem Solving George Lugar 2009 (6th edition) History of AI, search, knowledge representation, expert systems, rule-based systems, machine learning, evolutionary algorithms, automated reasoning, natural language understanding, etc. 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".
Artificial Intelligence: Foundations of Computational Agents David L. Poole, Alan K. Mackworth 2017 (2nd edition) Machine learning, search, planning, reasoning, and knowledge-based systems, etc. I occasionally consulted this book.
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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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