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Informatique et Mathématiques appliquées
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> Formation > Cursus ingénieur

INTELLIGENT SYSTEMS: RECOGNITION AND REASONING

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  • Number of hours

    • Lectures : 18.0
    • Tutorials : 18.0
    ECTS : 2.5

Goals

This course brings together programming techniques for recognition and symbolic reasoning. Techniques for symbolic reasoning are provided based on rule based programming and structured knowledge representations using schema. Programming of rules and schema are illustrated with exercises in the CLIPS Expert-System environment (developed by NASA). Techniques for recognition are presented based on Bayesian pattern recognition. Linear and quadratic discrimination functions are presented, followed by feature space reduction techniques based on the Fisher discriminant function and principal Components analysis. An introduction to learning theory is provided using the EM algorithm to estimate Gaussian Mixture Models. Lectures will be given in English.


Contact James CROWLEY

Content

Part 1: Reasoning with rule based expert systems.
1. Introduction to Expert Systems
2. Rule based programming methods
3. Structured knowledge Recognition
Part 2: Recognition and Learning
1. Introduction to Bayesian recognition
2. Discriminant functions
3. Learning with EM and Mixture Models.



Prerequisites

Applied Probabilities, Statistics

Tests

Written Exam (documents allowed) (E)



N1=E1
N2=E2

Additional Information

This course may be followed in french or in english  

Curriculum->MMIS.->Semester 4

Bibliography

  • Polycopié du cours / Course Notes
  • P. Lucas and L. Van de Gaag, Principles of Expert Systems Programming, Addison Wesley, 1991.
  • C. M. Bishop, Neural Networks for Pattern Recognition, Oxford University Press, 1994.

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French
Grenoble INP - Ensimag
École nationale supérieure d'informatique et de mathématiques appliquées
681, rue de la passerelle - Domaine universitaire - BP 72
38402 SAINT MARTIN D'HERES
 
 
République Française         Groupe INP
    Université Grenoble Alpes