Stanford machine learning lectures and POMDP

Came across this gem accidentally today. Here are the [complete 20 lectures for CS 229 Machine Learning from Stanford University][] for my own record. I have learned most of the material before, but have forgotten most of it already. In particular, the last lecture talks about POMDP. I have always thought it would be a great tool to be applied on a financial trading system. Long story short, POMDP is a distinctive machine learning algorithm because it inherently assumes the 'world' is partially-observable (MDP on its own takes care of the uncertainty part). A drawback with using artificial intelligence in trading is that A.I. are good for closed ended system but bad for open ended ones (i.e. trading). I believe POMDP can be worked on to become a valuable trading system. I haven't tried implementing it myself yet because of lack of a good platform and data. But since POMDP has been around for years now, I would assume somebody somewhere has tried this already. Unfortunately, I haven't been able to find any information on this problem. In any case, this post is merely a reminder for myself.

[complete 20 lectures for CS 229 Machine Learning from Stanford University]: http://www.youtube.com/view_play_list?p=A89DCFA6ADACE599