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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 2024/2025 **

** Teaching: **
Jiří Matas (JM) matas@fel.cvut.cz,
Ondřej Drbohlav (OD) drbohlav@fel.cvut.cz

** Where and when:** KN:E-301 at Building G, Karlovo namesti, Monday 16:15-17:45

** Lectures are streamed live ** on YouTube at the time of lecture:
online stream of the lecture room KN:E-301

** Recorded lectures **

Week | Date | Lect. | Slides | Topic | Wiki | Additional material | |
---|---|---|---|---|---|---|---|

1 | 23.9. | OD | Introduction. Basic notions. The Bayesian recognition problem | Machine_learning Naive_Bayes_classifier | some simple problems | ||

2 | 30.9. | JŠ | Non-Bayesian tasks / lecture given by Jonáš Šerých | 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. | no teaching | State holiday | ||||

7 | 4.11. | JM | Classifier training. Linear classifier. Perceptron. | Linear_classifier Perceptron | |||

8 | 11.11. | JM | SVM classifier | Support_vector_machine | demo | ||

9 | 18.11. | JM | Adaboost learning | Adaboost | |||

10 | 25.11. | JM | Neural networks. Backpropagation | Artificial_neural_network | |||

11 | 2.12. | JM | Cluster analysis, k-means method | K-means_clustering K-means++ | |||

12 | 9.12. | JM | 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 | Optimalizace (CZ): PCA slides, script 7.2 | ||

14 | 6.1. | 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

Conditions for assessment are in the lab section.

- Only students who receive all credits from the lab work and are granted the assessment (“zápočet”) can be examined.
- The labs contribute 50% to your final evaluation, the written part of the exam contributes 40% and the oral part 10%.
- For passing the written exam, at least 40% of maximum number of points is needed (16 out of 40 points).
- Your grade chances after the written exam test and before the oral exam are illustrated in the image below. Beware: The scheme is
**approximate only**, but illustrates how the oral exam influences the final grade. - The questions used in the test are available here (if one can solve these questions, one will likely do well in the exam).
- The oral starts approximately 2 hours after the end of the test, as soon as the tests are graded. Alternatively, the oral might take place the following day, if the number of tests to correct is high.
- The oral part is compulsory! Note that if during the oral examination it becomes clear that the student cannot explain how they obtained a result in the written exam, the examiner may re-assess the grading of the written exam.
- Example oral exam questions are available here.

courses/be5b33rpz/start.txt · Last modified: 2024/10/07 16:27 by drbohlav