Wow, this is awesome. I've been meaning to experiment with Hearthstone AI but coding the rules engine has been putting me off.
I've seen lots of Hearthstone projects in GitHub lately but most of them about getting data from the client. With all this wealth of code around putting together a bot is getting easier and easier. I wonder how Blizzard will react to this trend.
I don't know, requiring several phones within hearing range seems like it would severely limit the applicability of this technique.
If you think N phones provides at best N*(maximum sample rate of one phone) and that's assuming their phases a perfectly misaligned I'd think you'd still need quite a few to get something.
Plus the 100 Hz figure is optimistic. I dunno what kind of anti-aliasing these things have. If none you get an aliased signal and with a low order filter the practical maximum frequency detectable would be even less.
Learning is the ML-equivalent of what statisticians would call fitting a model. It usually involves optimizing a criterium such as the likelihood function of the data, the expected MSE under the posterior (Bayesian) or the margin of a linear classifier (SVM).
I always thought these Bitcoins might command a premium due to no counterparty risk and no need to give personal information to sketchy companies. Although from what I understand many bitcoin enthusiasts don't necessarily thrust the US government either...
A resource I've found invaluable and that I can't find listed is videolectures.net
Particularly http://videolectures.net/pascal/ has plenty of lectures and tutorials from their summer schools on very relevant topics for machine learning.
I think it's meant to compete with the likes of MATLAB, Python/Scipy and R which are probably the most used languages in scientific research.
As it stands now most prototyping is done in one of those high level languages and only if you really need the speedup do you port it to C/C++. Julia wants to be a high level dynamic language where you get the speedup for free.