 Because many developments in, and applications of, formal learning theory come from computer science, the term “computational learning theory” is also common. Many results on learning theory in computer science are concerned with Valiant’s and Vapnik’s notion of learning generalizations that are probably approximately correct (PAC learning) (Valiant [1984]). This notion of empirical success was introduced to philosophers by Gilbert Harmann in his Nicode lectures, and elaborated in a subsequent book [2007]. Valiant himself provides an accessible account of PAC learning and its relationship to the problem of induction in a recent book (Valiant [2013, Ch. 5]). The present article describes a nonstatistical tradition of learning theory stemming from the seminal work of Hilary Putnam [1963] and Mark E. Gold [1967]. Recent research has extended the reliabilist means-ends approach to a statistical setting where inductive methods assign probabilities to statistical hypotheses from random samples. The new statistical framework is described at the end of this entry, in the Section on  Reliable Statistical Inquiry. 