This course introduces statistical decision theory and surveys canonical and advanced classifiers such as perceptrons, AdaBoost, support vector machines, and neural nets.
Winter semester 2019/2020
Where and when: KN:G-205 at Building G, Karlovo namesti, Monday 12:45-14:15
Teaching: Jiří Matas (JM) matas@cmp.felk.cvut.cz, Ondřej Drbohlav (OD) drbohlav@cmp.felk.cvut.cz, Vojtěch Franc (VF) xfrancv@cmp.felk.cvut.cz, Boris Flach (BF) flachbor@cmp.felk.cvut.cz.
| Week | Date | Lect. | Slides | Topic | Wiki | Additional material | |
|---|---|---|---|---|---|---|---|
| 1 | 23.9. | JM | Introduction. Basic notions. The Bayesian recognition problem | Machine_learning Naive_Bayes_classifier | some simple problems | ||
| 2 | 30.9. | OD | Non-Bayesian tasks | Minimax | |||
| 3 | 7.10. | JM | Parameter estimation of probabilistic models. Maximum likelihood method | Maximum_likelihood | |||
| 4 | 14.10. | JM | Nearest neighbour method. Non-parametric density estimation. | K-nearest_neighbor_algorithm | |||
| 5 | 21.10. | JM | Logistic regression | Logistic_regression | |||
| 6 | 28.10. | – | (holiday, no lecture) | ||||
| 7 | 4.11. | JM | Classifier training. Linear classifier. Perceptron. | Linear_classifier Perceptron | |||
| 8 | 11.11. | JM | SVM classifier | Support_vector_machine | |||
| 9 | 18.11. | JM | Adaboost learning | Adaboost | |||
| 10 | 25.11. | JM | pdf, pdf, CNN Mishkin | Neural networks. Backpropagation | Artificial_neural_network | ||
| 11 | 2.12. | JM | Cluster analysis, k-means method | K-means_clustering K-means++ | |||
| 12 | 9.12. | JM | Unsupervised learning. EM (Expectation Maximization) algorithm. | Expectation_maximization_algorithm | Hoffmann,Bishop, Flach | ||
| 13 | 16.12. | JM | Feature selection and extraction. PCA, LDA. | Principal_component_analysis Linear_discriminant_analysis | Veksler, Franc, ver1 | ||
| 14 | 6.1. | JM | Decision trees. | Decision_tree Decision_tree_learning | Rudin@MIT |