  Training machine learning models in a meaningful order, from the easy samples
to the hard ones, using curriculum learning can provide performance
improvements over the standard training approach based on random data
shuffling, without any additional computational costs. Curriculum learning
strategies have been successfully employed in all areas of machine learning, in
a wide range of tasks. However, the necessity of finding a way to rank the
samples from easy to hard, as well as the right pacing function for introducing
more difficult data can limit the usage of the curriculum approaches. In this
survey, we show how these limits have been tackled in the literature, and we
present different curriculum learning instantiations for various tasks in
machine learning. We construct a multi-perspective taxonomy of curriculum
learning approaches by hand, considering various classification criteria. We
further build a hierarchical tree of curriculum learning methods using an
agglomerative clustering algorithm, linking the discovered clusters with our
taxonomy. At the end, we provide some interesting directions for future work.
