I. Statistical Pattern
Recognition Scheme
Design: Database, Feature Extraction, Classification, Validation
II. Feature Extraction
Clustering (KMeans), Principal Component Analysis (PCA)
III. Classification
Bayes Rule, Pattern Matching, Decision Trees, Parametric
Models (GMM)
IV. Introduction to
Neural Networks
Feed-Forward Neural Nets (Perceptron, MLP), Properties
Learning Rules (Backpropagation,
Rules, Cross-Validation)
Towards Deep Neural Networks (CNN)
V. Time Series
Dynamic Time Warping (DTW), Hidden Markov Models (HMM)
- Titulaire: Bernard GOSSELIN