  We introduce definitions of computable PAC learning for binary classification
over computable metric spaces. We provide sufficient conditions for learners
that are empirical risk minimizers (ERM) to be computable, and bound the strong
Weihrauch degree of an ERM learner under more general conditions. We also give
a presentation of a hypothesis class that does not admit any proper computable
PAC learner with computable sample function, despite the underlying class being
PAC learnable.
