RPZ Schedule RPZ students Discussion forum
This course introduces statistical decision theory and surveys canonical and advanced classifiers such as perceptrons, AdaBoost, support vector machines, and neural nets.
Winter semester 2018/2019
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 | 1.10. | JM | Introduction. Basic notions. The Bayesian recognition problem | Machine_learning Naive_Bayes_classifier | some simple problems | ||
2 | 8.10. | JM | Non-Bayesian tasks | Minimax | |||
3 | 15.10. | JM | Parameter estimation of probabilistic models. Maximum likelihood method | Maximum_likelihood | |||
4 | 22.10. | OD | Nearest neighbour method. Non-parametric density estimation. | K-nearest_neighbor_algorithm | |||
5 | 29.10. | JM | Logistic regression | Logistic_regression | |||
6 | 5.11. | JM | Classifier training. Linear classifier. Perceptron. | Linear_classifier Perceptron | |||
7 | 12.11. | JM | SVM classifier | Support_vector_machine | |||
8 | 19.11. | OD | Adaboost learning | Adaboost | |||
9 | 26.11. | JM | pdf, pdf | Neural networks. Backpropagation | Artificial_neural_network | ||
10 | 3.12. | JM | Cluster analysis, k-means method | K-means_clustering K-means++ | |||
11 | 10.12. | JM | Unsupervised learning. EM (Expectation Maximization) algorithm. | Expectation_maximization_algorithm | Hoffmann,Bishop, Flach | ||
12 | 17.12. | JM | Feature selection and extraction. PCA, LDA. | Principal_component_analysis Linear_discriminant_analysis | Veksler, Franc, ver1 | ||
13 | 31.12. | – | (holiday, no lecture) | ||||
14 | 7.1. | JM | Decision trees. | Decision_tree Decision_tree_learning | Rudin@MIT |