6883e621_ws___Machine_Learning__Theory___3
[0] Statistical Learning Theory [1] You assume that samples are drawn IID from an unknown distribution D. You think of learning as figuring out the number of samples required to distinguish a near-best hypothesis from a set of hypotheses. [2] There are substantially more positive results than for PAC Learning, and there are a few examples of practical algorithms directly motivated by this analysis. [3] 
The data is not IID. Ignorance of computational difficulties often results in difficulty of application. More importantly, the bounds are often loose (sometimes to the point of vacuousness).
All Models of Learning have Flaws « Machine Learning (Theory)
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