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Artificial Intelligence: A Modern Approach (2nd Edition) Paperback – January 1, 2002
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Otherwise this is a great CS book. Yes there is some math in it, but don't be scared - there is an appendix with all necessary mathematical background you'll need (and you don't need much). I was surprised to see so much historical references in this book, it teaches you not just about most major branches of AI, but also about how they started and where originated from in a "problem -> solution" form. For instance when they talk about genetic algorithms they actually go ahead and write a comprehensive comparison of analogies between biological evolution, genes and their computer-generated counterparts referencing the original work of Darwin and others.
If you're into AI, applied mathematics or computer science, I have no doubt you'll enjoy this book: it's not too focused on something specific (and something you'd need a PhD to understand) while not too shallow and covers fairly wide spectrum of AI problems, including (!) ethical and philosophical issues like "what happens if we succeed?"
My only complaint so far (not having finished the entire book) is that some of the definitions in chapter 17's whirlwind introduction to game theory were a little vague. But, a quick look at some other sources clarified things immensely.
It is rare to find a textbook as interesting and clear as this one. If a professor is requiring that you read it, consider yourself fortunate. If you are thinking of reading it yourself, you also are blessed. Look forward to many pleasant evenings.
As a student, I am often tempted to find ways to not purchase textbooks as you only end up using them for a semester but this is definitely one that you should spring for if you plan on doing AI work in the future.
1. No answer key for any problems. This feature has been standard in textbooks for decades as a way for students to self-check their understanding of the material.
2. Examples are scant and sometimes stop in the middle. For example, in Chapter 13, the example of applying Bayes' Rule gives one approach and indicates that it will discuss an alternative approach, but then the text just goes off on another path and never completes the example.
3. Inconsistent and (sometimes) convoluted pseudocode for the algorithms. Pseudocode should be a fairly-close-to-English approximation of the algorithm, but this book seems to mix RTL, English, and any other notation. Though the appendix includes an attempt at explaining their rationale behind their own brand of pseudocode, it's incomplete at best. Also, the function names don't follow any convention I've ever seen (I have 30+ years experience in software), and aren't even consistent within the book.
4. Condescending language. This should never occur in a textbook. In far too many places, the authors tell us that "the sharp-eyed reader will have noticed" or similar phrases, which basically implies, "if you didn't get our explanation and find the hidden subtext, you are not sharp-eyed". All such language should have been edited out.
The authors came so close to writing a classic, but sadly missed the mark. I think that any professors who claim that their students "universally love this book" are deluding themselves. Still, if your professor is good at explicating the material, it's worth going through it once, then switching to other materials, maybe primary source materials in the subfield(s) that grab your interest.
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