In this paper, a new notion of learnability is introduced, referred to as learnability with prior information (w.p.i.). This notion is weaker than the standard notion of probably approximately correct (PAC) learnability which has been much studied during recent years. A property called "dispersability" is introduced, and it is shown that dispersability plays a key role in the study of learnability w.p.i. Specifically, dispersability of a function class is always a sufficient condition for the function class to be learnable; moreover, in the case of concept classes, dispersability is also a necessary condition for learnability w.p.i. Thus in the case of learnability w.p.i., the dispersability property plays a role similar to the finite metric entropy condition in the case of PAC learnability with a fixed distribution. Next, the notion of learnability w.p.i. is extended to the distribution-free (d.f.) situation, and it is shown that a property called d.f. dispersability (introduced here) is always a sufficient condition for d.f. learnability w.p.i., and is also a necessary condition for d.f. learnability in the case of concept classes. The approach to learning introduced in the present paper is believed to be significant in all problems where a nonlinear system has to be designed based on data. This includes direct inverse control and system identification.