Assisting Transfer-Enabled Machine Learning Algorithms: Leveraging Human Knowledge for Curriculum Design

Matthew E. Taylor

Abstract: Transfer learning is a successful technique that significantly improves machine learning algorithms by training on a sequence of tasks rather than a single task in isolation. However, there is currently no systematic method for deciding how to construct such a sequence of tasks. In this paper, I propose that while humans are well-suited for the task of curriculum development, significant research is still necessary to better understand how to create effective curricula for machine learning algorithms.