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1.Introduction
mathematical preliminaries
2.Supervised Learning
National Day
3. Bayesian Decision Theory
4. Parametric Methods
4. Parametric Methods
5. Multivariate Methods
Multivariate Methods
6. Dimensionality Reduction (PCA)
6. Dimensionality Reduction (LDA)
7. Clustering (K-Means- Hierarchical)
7. Clustering (EM)
8. Nonparametric Methods
MIDTERM
9.Decision Trees
10. Linear Discrimination
11. Multilayer Perceptrons,
12. Kernel Machines
13. Hidden Markov Models,
14. Assessing and Comparing Classification Algorithms,
Presentations |