  Two different views on machine learning problem: Applied learning (machine
learning with business applications) and Agnostic PAC learning are formalized
and compared here. I show that, under some conditions, the theory of PAC
Learnable provides a way to solve the Applied learning problem. However, the
theory requires to have the training sets so large, that it would make the
learning practically useless. I suggest shedding some theoretical
misconceptions about learning to make the theory more aligned with the needs
and experience of practitioners.
