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In case you see the cheat sheet and think, "Wow, I'd love to understand that," there's an excellent (albeit challenging) complete course on machine learning in
by teuobk 15y ago
In case you see the cheat sheet and think, "Wow, I'd love to understand that," there's an excellent (albeit challenging) complete course on machine learning in Stanford's "engineering everywhere" online repository. http://see.stanford.edu/see/courseinfo.aspx?coll=348ca38a-3a6d-4052-937d-cb017338d7b1 http://see.stanford.edu/see/courseinfo.aspx?coll=348ca38a-3a...
- natfriedman 15y agoAnother option is "Programming Collective Intelligence," by Toby Segaran. I read through it recently on a long flight to Australia. It's one of the most straight-forward AI books out there, presenting most of these algorithms in just a few pages with nice sample Python code and diagrams. A perfect intro/refresher, and it takes a web developer perspective on these techniques. Since reading it I've noticed how many friends have it on their bookshelves. Here's a link: http://oreilly.com/catalog/9780596529321 http://oreilly.com/catalog/9780596529321
- klochner 15y agoI haven't read the COIN book, but if you want to get aggressive you can go for "Elements of Statistical Learning". Free pdf download, probably not a one-flight book: http://www-stat.stanford.edu/~tibs/ElemStatLearn/ http://www-stat.stanford.edu/~tibs/ElemStatLearn/ side note: Nat, did you intern at SGI in the late 90s, as the self-titled "armchair programmer of the apocalypse"?
- silentbicycle 15y agoWhile it does a great job of explaining many AI concepts in an unintimidating fashion, the Python code in it is rather buggy. On the balance, I'd still recommend it as an intro. The errata page: http://oreilly.com/catalog/errataunconfirmed.csp?isbn=9780596529321 http://oreilly.com/catalog/errataunconfirmed.csp?isbn=978059...
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