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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 |

- Duda R.O., Hart, P.E.,Stork, D.G.: Pattern Classification, John Willey and Sons, 2nd edition, New York, 2001
- Schlesinger M.I., Hlaváč V.: Ten Lectures on Statistical and Structural Pattern Recognition, Springer, 2002
- Bishop, C.: Pattern Recognition and Machine Learning, Springer, 2011
- Goodfellow, I., Bengio, Y. and Courville, A.: Deep Learning, MIT Press, 2016. www

- Only students that receive all credits from the lab work and receive (“zápočet”) can be examined. The labs contribute 50% to your final evaluation. Any extra credits beyond 50% will be considered at the final evaluation and may improve your mark.
- The exam consists of two parts: written test and oral exam.
- The written test lasts 60-90 minutes and contributes 40% to the final evaluation.
- The questions used in the test are available here (if one can solve these questions, one will likely do well on the exam)
- Oral part starts approximately 2 hours after the end of the test (the interim time is used to correct the tests). It contributes to the final evaluation by 10%.
- To get grade “A” for the course, “A” or “B” result of the final written exam is required.
- Oral exam questions are available here.

courses/be5b33rpz/lectures/start.txt · Last modified: 2018/11/30 14:07 by drbohlav