6883e621_ws___Machine_Learning__Theory___3
[0] PAC Learning [1] You assume that samples are drawn IID from an unknown distribution D. You think of learning as finding a near-best hypothesis amongst a given set of hypotheses in a computationally tractable manner. [2] The focus on computation is pretty right-headed, because we are ultimately limited by what we can compute. [3] 
There are not many substantial positive results, particularly when D is noisy. Data isn’t IID in practice anyways.
All Models of Learning have Flaws « Machine Learning (Theory)
3