UMR CNRS 7253

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en:sci22 [2013/10/02 11:18] – tdenoeuxen:sci22 [2014/08/10 13:03] – tdenoeux
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 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. 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.
  
-**Program (Fall 2013)**+**Program (Fall 2014)**
  
-  - 11 September: Introduction to uncertainty {{en:sci22_uncertainty.pdf|lecture notes}} +  - 10 September: Introduction to uncertainty  
-  - 18 September: Representation of evidence {{en:sci22_representation.pdf|lecture notes}}, {{en:sci22_td1.pdf|exercises}} +  - 17 September: Representation of evidence  
-  - 25 September: Combination of evidence. {{en:sci22_combination.pdf|lecture notes}}, {{en:sci22_td2.pdf|exercises}} +  - 1 October: Combination of evidence 
-  - 2 October: Coarsening and refinement. {{en:sci22_refinement_coarsening.pdf|lecture notes}} +  - 4 October: Least commitment principle. Coarsening and refinement 
-  - 5 October: Decision analysis. Classification and clustering +  - 8 October: Decision analysis. Classification and clustering 
-  - 9 October:  Applications  +  - 15 October:  Applications  
-  - 12 October: Presentation of research papers by students+  - 22 October: Presentation of research papers by students
  
  

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en/sci22.txt · Last modified: 2014/09/12 11:29 by tdenoeux
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