Number of hours
- Lectures 18.0
- Tutorials 18.0
ECTS
ECTS 2.5
Goal(s)
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(s)
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
Test
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.