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I am asking this question to get a list of standard textbooks read by top users or any user of our main site.

There are many domains in and around artificial intelligence such as machine learning, reinforcement learning, deep learning, probabilistic graphical models, optimization, etc., One may be a subset of another or can be overlapping.

Although there are tags in our main site that allows us to ask for recommendations, they did not contain all the books read by a user of our site.

The answers to this question also allow new users to check or read the books to understand and to ask new questions that have high chances of getting an answer.

What are the domains and the textbooks of that particular domain you read?


I am providing a sample answer for reference here:

Mathematics

Probability

  1. Introduction to Probability Book by Dimitri Bertsekas and John Tsitsiklis

  2. A FIRST COURSE IN PROBABILITY by Sheldon Ross

  3. ................

Stastistics

  1. ........................................
  2. ......................................
  3. ............................

Core

Deep Learning

  1. ................................
  2. .................................

Graphical Models

  1. ...................................

and so on..................

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    $\begingroup$ I'm not sure whether meta is a good place to discuss this (check here: ai.stackexchange.com/help/privileges/participate-in-meta, ai.stackexchange.com/help/whats-meta), but I like the idea of sharing with all community members the "background" (in AI, of course) that (experienced) users have acquired by reading books. I will ask other moderators whether they think it's a good idea to have this post or not. $\endgroup$
    – nbro Mod
    May 14 at 18:35
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    $\begingroup$ Another moderator from another site pointed out that a similar experiment (type of question) worked well on another site. See here, although the question seems to be slightly different, as it's not asking for what the experienced users have really read, which is what you're asking. So, I think we could also allow it. Meanwhile, when I have more time, maybe I will prepare an answer, which shouldn't be very long anyway :D $\endgroup$
    – nbro Mod
    May 16 at 22:56
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I don't think I have ever fully read any technical book or textbook (on an AI-related subject/topic). Typically, I start reading a book, then, after a while, I don't have more time to read it, so I interrupt the reading, which I may resume later.

So, I have partially read multiple books and consulted others multiple times. Below, I list some of the books that I read or consulted the most, as far as I remember. There are probably other books that I've partially read. Currently, I'm also reading another book (not mentioned below), and I may start reading another one. I will be updating this list.

Artificial Intelligence

  • Reinforcement Learning: An Introduction (by Barto and Sutton, this is probably the book I read the most, almost all chapters of the first edition, and I regularly consult it)
  • Artificial Intelligence: A Modern Approach (by Russell and Norvig, I read several chapters, especially the ones related to search, and I often consult it)
  • Superintelligence: Paths, Dangers, Strategies (by Bostrom, the first chapters, then I got tired of reading the speculations)
  • Universal Artificial Intelligence: Sequential Decisions Based On Algorithmic Probability (by Hutter I think only the first and maybe second chapters; very technical book, so it's not an easy reading at all, even if you're familiar with the theory of computation)
  • Multiple view geometry in computer vision (by Richard Hartley; this book introduces some traditional computer vision topics; I didn't read much)

Computer Science

  • Introduction to Algorithms (by CLRS, any computer scientist should be aware of this book; do you want to know about red-black trees or binary search? this is the book!)
  • Introduction to the Theory of Computation (by Sipser, which is one of my favorite books, among all of these; do you want to know about finite-state machines, push-down automata, Turing machines, formal languages, the pumping lemma, the Halting problem, undecidability, complexity theory [e.g. NP-completeness], reducibility, etc.)? This is the book you should read!)
  • Quantum Computing for Computer Scientists (by Mannucci and Yanofsky, which is also one of my favorite books)
  • Computational Geometry: Algorithms and Applications (by Mark de Berg et al., I didn't read much)
  • Structured Computer Organization (by Andrew S. Tanenbaum; we had used this book in a course "Computer Architecture" during the first year of my bachelor's in CS; I don't remember what I had read...)

Math

  • Elementary Analysis: The Theory of Calculus (by Ross; this is a very nice book that will help you consolidate your knowledge of calculus, if you go through the details, proofs, etc.; my professor, when I was doing my bachelor's, in fact, noticed, from my one of my exams, that I had read the book; I didn't fully read it, only a few chapters, but it's a book to keep an eye on if you want to have a solid knowledge of the topic)
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  • $\begingroup$ Great post! I need to add these to my library, since my current program is not rigorous. (I've been using Bohm for quantum mechanics, but, so far, I've only been interested in fundamental definitions, such as his expression of orthogonality in ℝⁿ. Now that I've forgotten other expressions, I want to try and create my own expression in ℤ-space for n-dimensional regular grids, which should be trivial. Sipser sounds like it would be a great help in general for formal statements.) $\endgroup$
    – DukeZhou Mod
    Aug 31 at 2:47
  • $\begingroup$ And it seems like, if I were able to do this, I could then check Berg to see if it's consistent. I found Tanenbaum 2016 for $4 in paperback. There's a million textbooks that look good, but they don't come with a trusted recommendation. I bought the Bohm b/c I was seeing him him cited everywhere. $\endgroup$
    – DukeZhou Mod
    Aug 31 at 2:59

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