Learning at multiple resolutions provides a fast, hierarchical and efficient technique for extracting models from empirical data.  In this chapter we describe the application of wavelets for multi-resolution learning in artificial neural networks and inductive decision trees, and show how wavelets may provide a unifying framework for various supervised learning techniques.  A Wave-Net is an artificial neural network with activation functions derived from the class of wavelets.  Wave-Nets combine the mathematically rigorous, multi-resolution character of wavelets with the adaptive learning of artificial neural networks.  Learning with Wave-Nets is efficient, and is explicitly based on the local or global error of approximation.   The advantages of Wave-Net learning over other artificial neural learning techniques are highlighted, and learning methods for minimizing the L2 or L° norms are described.  The reduced black box character of Wave-Nets is demonstrated by the explicit relationship between Wave-Net parameters and the quality of learning, and by the ability to extract if-then rules from a Haar Wave-Net.  The relationship between Haar Wave-Nets and other rule-extraction techniques such as decision trees is described.