

Stanford Machine Learning Course - helwr
http://www.youtube.com/watch?v=UzxYlbK2c7E

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mindcrime
There's a pile of great AI/ML content up on Youtube as well. IIT has posted
two cool AI series that HN'ers might like. See:

[http://www.youtube.com/watch?v=eLbMPyrw4rw&feature=PlayL...](http://www.youtube.com/watch?v=eLbMPyrw4rw&feature=PlayList&p=2F21126C45E3213F&playnext_from=PL&index=0&playnext=1)

and/or

[http://www.youtube.com/watch?v=fV2k2ivttL0&feature=PlayL...](http://www.youtube.com/watch?v=fV2k2ivttL0&feature=PlayList&p=7DDB194A77DF884F&playnext_from=PL&index=0&playnext=1)

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patrickmclaren
These lectures follow Norvig's AI: A Modern Approach. If self studying, I find
it helps a great deal to reinforce what you've read through watching these
lectures.

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silkodyssey
Additional content (transcripts/handouts/assignments) are available at the
Standford website.

[http://see.stanford.edu/see/lecturelist.aspx?coll=348ca38a-3...](http://see.stanford.edu/see/lecturelist.aspx?coll=348ca38a-3a6d-4052-937d-cb017338d7b1)

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kogir
Andrew Ng is great. He's worked on some really cool and practical stuff. Check
out his projects:

<http://www.cs.stanford.edu/people/ang/research.html>

I helped build parts of the hardware and control software for Retiarius and
the Snake robot:

<http://www.cs.stanford.edu/people/ang/rl-videos/>

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imp
Some people have been working through this class together on Curious Reef.
It's loosely organized, with people posting questions and ideas in the class
forum: [http://curiousreef.com/class/stanford-cs229-machine-
learning...](http://curiousreef.com/class/stanford-cs229-machine-
learning/forum) Might be a useful resource to people learning this material.
(Disclosure: it's my website)

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brown9-2
Thanks for posting this. The course is on iTunesU also, if anyone wants to
download the files to sync to your iPod or iPhone.

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mmaunder
I was hoping for a video thick with data I could apply or use to Google and
learn more.

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jules
This is good but not excellent. Too much theory motivated by theory motivating
more theory, whereas in the real world the theory is motivated by and usually
invented after practice.

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jey
Huh? You use the theory to figure out what to implement in practice.

The idea of just randomly hacking some shit together then backfitting a theory
onto it is absurd. That's the same strategy that led to many of the past
failures of "AI" -- approaches based too much on intuition that wasn't
theoretically well grounded.

Probability theory and statistical models are foundational material for anyone
interested in machine learning.

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madmanslitany
There's a great applicable quote from Nikola Tesla here: “If Edison had a
needle to find in a haystack, he would proceed at once with the diligence of
the bee to examine straw after straw until he found the object of his search.
I was a sorry witness of such doings, knowing that a little theory and
calculation would have saved him ninety per cent of his labor.”

~~~
gjm11
As I just pointed out to someone else who quoted the exact same thing
elsewhere on HN, that approach seemed to work out pretty well for Edison.

