course: 2AMU30

This course introduces students to advanced theories of uncertainty that can be used in data science, artificial intelligence, and machine learning for representing and reasoning with knowledge. Concretely, it covers belief functions, possibility theory, fuzzy sets, credal sets, and general imprecise probability theories. These theories are ideal for enhancing our capabilities to provide robust and interpretable results. We treat various aspects: modelling principles, algorithms for learning and reasoning, and the application to decision making. The theories shine when learning from relatively limited amounts of data or when robustness is required. So, the course discusses robust decision making for scenarios of high risk or with stringent safety requirements.

Details

Level:

Master

Quartile:

Q1

Lecturers:

Erik Quaeghebeur

Thomas Krak