A concept class F is PAC learnable if for all targets f E F and all probability distributions P on the sample space U* the learning algorithm A outputs a concept g E F such that with probability (1-5) it holds that we have a chance on an error with P(f Ag) _< e (where fag = (f -g)U(g-f)) We are especially interested in concept classes that are defined by some formalism (language).
a language can describe come collection of concepts.