  Humans, as the most powerful learners on the planet, have accumulated a lot
of learning skills, such as learning through tests, interleaving learning,
self-explanation, active recalling, to name a few. These learning skills and
methodologies enable humans to learn new topics more effectively and
efficiently. We are interested in investigating whether humans' learning skills
can be borrowed to help machines to learn better. Specifically, we aim to
formalize these skills and leverage them to train better machine learning (ML)
models. To achieve this goal, we develop a general framework -- Skillearn,
which provides a principled way to represent humans' learning skills
mathematically and use the formally-represented skills to improve the training
of ML models. In two case studies, we apply Skillearn to formalize two learning
skills of humans: learning by passing tests and interleaving learning, and use
the formalized skills to improve neural architecture search. Experiments on
various datasets show that trained using the skills formalized by Skillearn, ML
models achieve significantly better performance.
