This course introduces statistical decision theory and surveys canonical and advanced classifiers such as perceptrons, AdaBoost, support vector machines, and neural nets.

** Winter semester 2020/2021 **

Due to the covid-19 situation, the **lectures will be given online, via zoom**. All students enrolled in KOS will be sent a link at 12:30.

** 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

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

1 | 21.9. | JM | pdf recording | Introduction. Basic notions. The Bayesian recognition problem | Machine_learning Naive_Bayes_classifier | some simple problems | |

2 | 28.9. | – | (holiday, no lecture) | ||||

3 | 5.10. | JM | pdf recording | Non-Bayesian tasks | Minimax | ||

4 | 12.10. | JM | pdf recording | Parameter estimation of probabilistic models. Maximum likelihood method | Maximum_likelihood | ||

5 | 19.10. | JM | pdf recording | Nearest neighbour method. Non-parametric density estimation. | K-nearest_neighbor_algorithm | ||

6 | 26.10. | JM | pdf recording | Logistic regression | Logistic_regression | ||

7 | 2.11. | JM | pdf recording | Classifier training. Linear classifier. Perceptron. | Linear_classifier Perceptron | ||

8 | 9.11. | JM | pdf recording | SVM classifier | Support_vector_machine | ||

9 | 16.11. | JM | pdf recording | Adaboost learning | Adaboost | ||

10 | 23.11. | JM | Neural networks. Backpropagation | Artificial_neural_network | |||

11 | 30.11. | JM | Cluster analysis, k-means method | K-means_clustering K-means++ | |||

12 | 7.12. | JM | Unsupervised learning. EM (Expectation Maximization) algorithm. | Expectation_maximization_algorithm | Hoffmann,Bishop, Flach | ||

13 | 14.12. | JM | Feature selection and extraction. PCA, LDA. | Principal_component_analysis Linear_discriminant_analysis | Veksler, Franc, ver1 | ||

14 | 4.1. | 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: 2020/11/24 19:39 by matas