  Curriculum design is a fundamental component of education. For example, when
we learn mathematics at school, we build upon our knowledge of addition to
learn multiplication. These and other concepts must be mastered before our
first algebra lesson, which also reinforces our addition and multiplication
skills. Designing a curriculum for teaching either a human or a machine shares
the underlying goal of maximizing knowledge transfer from earlier to later
tasks, while also minimizing forgetting of learned tasks. Prior research on
curriculum design for image classification focuses on the ordering of training
examples during a single offline task. Here, we investigate the effect of the
order in which multiple distinct tasks are learned in a sequence. We focus on
the online class-incremental continual learning setting, where algorithms or
humans must learn image classes one at a time during a single pass through a
dataset. We find that curriculum consistently influences learning outcomes for
humans and for multiple continual machine learning algorithms across several
benchmark datasets. We introduce a novel-object recognition dataset for human
curriculum learning experiments and observe that curricula that are effective
for humans are highly correlated with those that are effective for machines. As
an initial step towards automated curriculum design for online
class-incremental learning, we propose a novel algorithm, dubbed Curriculum
Designer (CD), that designs and ranks curricula based on inter-class feature
similarities. We find significant overlap between curricula that are
empirically highly effective and those that are highly ranked by our CD. Our
study establishes a framework for further research on teaching humans and
machines to learn continuously using optimized curricula.
