UMR CNRS 7253

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en:sci22 [2013/09/10 15:23] tdenoeuxen:sci22 [2014/09/12 11:29] tdenoeux
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 **Instructor:** Thierry Denoeux **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)** Room O223 (PG2)
  
-  - 11 September: Introduction to uncertainty {{en:lecture1.pdf|pdf}} +  - 10 September: Introduction to uncertainty  
-  - 18 September: Representation of evidence +  - 17 September: Representation of evidence  
-  - 25 September: Combination of evidence. Classification +  - 1 October: Combination of evidence 
-  - October: Inclusion relations, uncertainty measures and the minimal commitment principle +  - October: Least commitment principle. Coarsening and refinement 
-  - October: Refinement and coarsening of a frame of discernment. Object asociation +  - October: Decision analysis. Classification and clustering 
-  - October: Decision analysis. Clustering +  - 15 October:  Applications  
-  - 12 October: Presentation of research papers by students+  - 18 October: Presentation of research papers by students
  
  

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