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--- en:sci22 [2013/09/25 11:32] – tdenoeux
+++ en:sci22 [2014/09/12 11:29] (current) – tdenoeux
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 **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.
+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 (including interval analysis). Numerous examples taken from different domains (Artificial Intelligence, multi-sensor fusion, numerical modeling) will illustrate the theoretical notions.
-**Programme (Fall 2013)**
+**Program (Fall 2014)**
-  - 11 September: Introduction to uncertainty {{en:sci22_uncertainty.pdf|lecture notes}}
+Room O223 (PG2)
-  - 18 September: Representation of evidence {{en:sci22_representation.pdf|lecture notes}}, {{en:sci22_td1.pdf|exercises}}
-  - 25 September: Combination of evidence. {{en:sci22_combination.pdf|lecture notes}}, {{en:sci22_td2.pdf|exercises}}
+  - 10 September: Introduction to uncertainty
-  - 2 October: Coarsening and refinement. Decision analysis.
+  - 17 September: Representation of evidence
-  - 5 October: Applications 1
+  - 1 October: Combination of evidence
-  - 9 October:  Applications 2
+  - 4 October: Least commitment principle. Coarsening and refinement
-  - 12 October: Presentation of research papers by students
+  - 8 October: Decision analysis. Classification and clustering
+  - 15 October:  Applications
+  - 18 October: Presentation of research papers by students

UMR CNRS 7253

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