Curriculum-Based Learning with STiNT Whereas much work in machine learning addresses how to learn a single function in isolation, I have been interested in how to boost learning accuracy when multiple functions are learned over time. In this talk I will address two coupled questions: (1) what algorithms can be developed that benefit from previous learning when presented with a new function to be learned? and (2) what curriculum, or ordering of functions, produces the most effective learning? (This notion of curricula occurs naturally in situations such as robot learning). I'll outline a space of algorithms that can benefit from curricula, and will examine one in detail: STiNT. STiNT combines multiple neural networks in a directed acyclic graph, so as to benefit from previously learned functions. STiNT extends that graph as new functions to be learned arrive so as to approximately maximize the benefit from the curriculum. By exploring the performance of random, optimal, maximally pessimistic, and hand-coded curricula, I'll show that both that using a curriculum is a good thing to do, although the accuracy of learning with STiNT is strongly influenced by the curriculum used. A neat thing about STiNT is that a simple greedy algorithm can efficiently use explicit domain theories and statistical tests to automatically generate a curriculum that approximates the performance of the optimal curriculum. * (This is joint work with Tom Mitchell).