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Informatique et Mathématiques appliquées
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Intelligent Systems: Reasoning and Recognition - WMM42E5

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  • Number of hours

    • Lectures : 31.5
    ECTS : 3.0

Goals

This course brings together programming techniques symbolic reasoning and Bayesian Pattern Recognition. 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 Bayesian pattern recognition include Linear and quadratic discrimination functions 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, as well as linear pattern detectors, boosted learning, kernel methods and support vector machines. All lectures are 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.
4. Linear Detection functions, boosted learning and SVM.



Prerequisites

Probability Theory (Ensimag 1st year cours)

Tests

The exam is given in english only 

Written Exam (documents allowed)



N1=E1
N2=E2

Additional Information

This course is given in english only EN

Curriculum->MS in Informatics at Grenoble->MOSIG - Semester 2

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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Université Grenoble Alpes