Pattern recognition is related to the design and study of systems that can learn by automatically extracting information from data. An example of application is classification, which consists of assigning each input data to a class. This course offers a broad introduction to pattern recognition and its applications to real problems. A subset of the following is covered: principal component analysis, bayesian decision theory, density estimation, etc.
- Introduction [pdf]
- Rappels généraux et vocabulaire [pdf] [exercices]
- Analyse en composantes principales [pdf] [exercices] [data.mat]
- Analyse factorielle discriminante [pdf] [exercices]
- Régression linéaire. Extensions à la régression logistique [pdf] [exercices]
- Méthodes décisionnelles bayésiennes