Lectures (3 hours every meeting)
1 21.3.06 - General Introduction to Visual Recognition (Rivlin). Bayesian Decisions
(Adam)
2 4.4.06 - Decision Theory: Bayesian, Minimax and Neyman-Pearson Criteria (Adam)
3 11.4.06 - Recognition from Function (Rivlin)
4 25.4.06 - Principal Components Analysis (Adam) (1 hour)
5 9.5.06 - Principal Components and SVD (continued)
6 16.5.06 - Linear classification by regression on the indicators matrix, Linear and
Quadratic Discriminant Analysis, Reduced Rank LDA (+ Fisher's approach)
(Adam)
7 23.5.06 - Linear classifiers by explicit construction, Perceptron algorithm, maximum
margin classifiers, support vector machines (Adam)
8 30.5.06 - ICA, density estimation (Adam)