  For many years, researchers in psychology, education, statistics, and machine
learning have been developing practical methods to improve learning speed,
retention, and generalizability, and this work has been successful. Many of
these methods are rooted in common underlying principles that seem to drive
learning and overlearning in both humans and machines. I present a review of a
small part of this work to point to potentially novel applications in both
machine and human learning that may be worth exploring.
