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It looks like a stripped down version of AIMA (http://aima.cs.berkeley.edu/ http://aima.cs.berkeley.edu/) with two differences: It doesn't cram everything into
by seiji 19y ago
It looks like a stripped down version of AIMA (http://aima.cs.berkeley.edu/ http://aima.cs.berkeley.edu/) with two differences: It doesn't cram everything into an "agent based approach" and it gives usable examples right away skipping theory (no 100 pages devoted to first order logic).
In the sample text they mention Hot or Not, Google, Amazon, Netflix, and a few other companies to quickly give a "real world" view of what is useful. The book certainly will get more people interested in doing more sophisticated computations on their data. (Not that I think AIMA using Romanian cities or endless "sue is pat's mother. sarah is pat's daughter => sue is sarah's grandmother" examples make the material seem less useful.)
- neilc 19y agoIt doesn't seem like a "stripped down version of AIMA" at all, IMHO. AIMA is an AI book, this is an introductory data mining book. As such, this book talks about algorithms for clustering, classification, feature identification, collaborative filtering, etc., none of which are really addressed in depth in AIMA. There is a relatively brief section on learning techniques in AIMA, but it doesn't go into much depth, and is more focused on reinforcement learning than on typical data mining techniques (automatic classification, clustering, etc.)
- plinkplonk 19y agoDoes the book go into any depth on these issues or is the coverage superficial? Is there code? Thanks in advance