In this seminar, I will discuss a foundation for inductive learning based on the use of higher-order logic for knowledge representation. In particular, the approach (i) provides a systematic individuals-as-terms approach to knowledge representation for inductive learning, and demonstrates the utility of types and higher-order constructs for this purpose; (ii) gives a systematic way of constructing predicates for use in induced definitions; (iii) widens the applicability of decision-tree algorithms beyond the usual attribute-value setting to the classification of individuals with complex structure; and (iv) shows how to induce definitions which are comprehensible and have predictive power. I will present some illustrative applications involving a variety of types to which a decision-tree learning system based on the approach has been applied. (Knowledge of higher-order logic and decision-tree learning will not be presumed.)