
Building an Accurate Statewide Dropout Early Warning System - luu
http://jaredknowles.com/journal/2014/8/24/of-needles-and-haystacks-building-an-accurate-statewide-dropout-early-warning-system-in-wisconsin
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therobot24
This system very much reminds me of a 'talking machines' episode, where a
physics grad student asked a question concerning the definition of "machine
learning" when most systems are hand crafted and the actual _learning_ is just
statistics of the data put through the system.

Here we have a very specific system and they test over different
instantiations of the approach, reporting one works better than the other. So
what did we learn? For that matter, what did the machine learn? I'll have to
read the paper more closely to really get the meat but, it's definitely an
interesting problem.

On a side note I absolutely love seeing stuff on HN concerning ML being
applied to stuff other than "big data"/"deep learning" startups. More links
like these please!

