Learning theory is an active research area that incorporates ideas,
problems, and techniques from a wide range of disciplines including
statistics, artificial intelligence, information theory, pattern
recognition, and theoretical computer science. The research reported
at the 21st International Conference on Algorithmic Learning Theory
(ALT 2010) ranges over areas such as query models, online learning,
inductive inference, boosting, kernel methods, complexity and
learning, reinforcement learning, unsupervised learning, grammatical
inference, and algorithmic forecasting. In this introduction we give
an overview of the five invited talks and the regular contributions
of ALT 2010
