Probably Approximately Correct Learning and Related Topics

Probably Approximately Correct (PAC) learning is a theoretical framework that addresses the problem of learning a function from a set of samples in a way that is both probably correct and approximately correct. In simpler terms, PAC learning formalizes the conditions under which a learning algorithm can be expected to perform well on new, unseen data after being trained on a finite set of examples.
PAC learning is concerned with the feasibility of learning in a probabilistic sense. It asks whether there exists an algorithm that, given enough examples, will find a hypothesis that is approximately correct with high probability. The "probably" aspect refers to the confidence level of the algorithm, while the "approximately correct" aspect refers to the accuracy of the hypothesis.

The tutorial is scheduled for 25 August 2026, from 17:00 to 19:15, and 26 August 2026, from 9:30 to 12:00.

Confirmed Lecturers

• Pascal Germain (Département d’Informatique et de Génie Logiciel, Université Laval, Québec, Canada)
• Ata Kaban (School of Computer Science, University of Birmingham, UK)

Last update:  11 February 2026.