List of competencies you should have after individual lectures. (Will be updated continuously.)
Work in prgress, update for the upcoming semester.
| datum | č.t. | S/L | náplň |
|---|---|---|---|
| 20.02.2018 | 1. | S | |
| 27.02.2018 | 2. | L | |
| 06.03.2018 | 3. | S | |
| 13.03.2018 | 4. | L | |
| 20.03.2018 | 5. | S | |
| 27.03.2018 | 6. | L | |
| 03.04.2018 | 7. | S | |
| 10.04.2018 | 8. | L | |
| 17.04.2018 | 9. | S | |
| 24.04.2018 | 10. | L | |
| 01.05.2018 | 11. | S | Svátek |
| 08.05.2018 | 12. | L | Svátek |
| 15.05.2018 | 13. | S | |
| 17.05.2018 | 13. | S | Výuka jako v úterý |
| 22.05.2018 | 14. | L |
| Date | W# | Who | Contents | Materials |
|---|---|---|---|---|
| 21.02.2017 | 1 | RM | AI, PR, learning and robotics. Decision tasks. Empirical learning. | P01.LearningBayesStrategy.pdf |
| 28.02.2017 | 2 | PP | Linear methods for classification and regression. | Slides. Handouts. |
| 07.03.2017 | 3 | PP | Non-linear models. Feature space straightening. Overfitting. | Slides. Handouts. |
| 14.03.2017 | 4 | PP | Nearest neighbors. Kernel functions, SVM. Decision trees. | Slides. Handouts. |
| 21.03.2017 | 5 | PP | Bagging. Adaboost. Random forests. | Slides. Handouts. |
| 28.03.2017 | 6 | PP | Probabilistic graphical models. Bayesian networks. | Slides. Handouts. |
| 04.04.2017 | 7 | PP | Hidden Markov models. | Slides. Handouts. |
| 11.04.2017 | 8 | PP | Expectation-Maximization algorithm. | Slides. Handouts. |
| 18.04.2017 | 9 | RM | Planning. Planning problem representations. Planning methods. | Handouts |
| 25.04.2017 | 10 | RM | Scheduling. Local search. | Handouts |
| 02.05.2017 | 11 | No lecture - schedule as on even Monday | ||
| 09.05.2017 | 12 | RM | Constraint satisfaction problems. | Handouts |
| 16.05.2017 | 13 | PP | Neural networks. Basic models and methods, error backpropagation. | Slides. Handouts. |
| 23.05.2017 | 14 | PP | Deep learning. Convolutional and recurrent NNs. | Slides. Handouts. |