Instructor: Thierry Denoeux

Description: This course presents the main formalisms for uncertainty representation with emphasis on the theory of belief functions, which generalizes both probability theory and the set-membership approach (inclusing interval analysis). Numerous examples taken from different domains (Artificial Intelligence, multi-sensor fusion, numerical modeling) will illustrate the theoretical notions.

Programme (Fall 2013)

11 September: Introduction to uncertainty
18 September: Belief functions: representation of evidence 25 September: Belief functions: combination of evidence 2 October: Refinement and coarsening of a frame of discernment 5 October: 9 October: Applications to pattern recognition and information fusion 12 October: Presentation of research papers by students