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)

1. 11 September: Introduction to uncertainty lecture notes
2. 18 September: Representation of evidence lecture notes, exercises
3. 25 September: Combination of evidence. lecture notes, exercises
4. 2 October: Coarsening and refinement. lecture notes
5. 5 October: Decision analysis. Classification and clustering
6. 9 October: Applications
7. 12 October: Presentation of research papers by students