List of competencies you should have after individual lectures. (Will be updated continuously.)
The order of lectures is subject to change.
Date | W# | Who | Contents | Materials |
---|---|---|---|---|
19.02.2019 | 1 | PP | AI, PR, learning and robotics. Decision tasks. Empirical learning. | Slides. Handouts. |
26.02.2019 | 2 | PP | Linear methods for classification and regression. | Slides. Handouts. |
05.03.2019 | 3 | RM | Non-linear models. Feature space straightening. Overfitting. | Slides. Handouts. |
12.03.2019 | 4 | PP | Nearest neighbors. Kernel functions, SVM. Decision trees. | Slides. Handouts. |
19.03.2019 | 5 | PP | Bagging. Adaboost. Random forests. | Slides. Handouts. |
26.03.2019 | 6 | PP | Neural networks. Basic models and methods, error backpropagation. | Slides. Handouts. |
02.04.2019 | 7 | PP | Deep learning. Convolutional and recurrent NNs. | Slides. Handouts. |
09.04.2019 | 8 | PP | Probabilistic graphical models. Bayesian networks. | Slides. Handouts. |
16.04.2019 | 9 | PP | Hidden Markov models. | Slides. Handouts. |
23.04.2019 | 10 | PP | Expectation-Maximization algorithm. | Slides. Handouts. |
30.04.2019 | 11 | RM | Planning. Planning problem representations. Planning methods. | Handouts |
07.05.2019 | 12 | RM | Constraint satisfaction problems. | Handouts |
14.05.2019 | 13 | No lecture. Schedule as on wednesday. | ||
21.05.2019 | 14 | RM | Scheduling. Local search. | Handouts |