| Date | Lecture | Teacher | Materials | Notes / Reading |
|---|---|---|---|---|
| 16.02.2022 | 1. Introduction to Neural Networks | BF | slides record | McCulloch-Pitts Neuron |
| 23.02.2022 | 2. The Power of Neural Networks | BF | slides record | |
| 02.03.2022 | 3. Backpropagation | AS | slides record | |
| 09.03.2022 | 4. Stochastic Gradient Descent (SGD) | AS | slides record | |
| 16.03.2022 | 5. Convolutional Neural Networks | AS | slides record | |
| 23.03.2022 | 6. Data Augmentation, Weight Initialization, Batch Normalization | BF | slides record | |
| 30.03.2022 | 7. Regularization Methods for NNs | AS | slides record | |
| 06.04.2022 | 8. Adaptive SGD Methods | AS | slides record | |
| 13.04.2022 | 9. Adversarial Patterns. Robust Learning Approaches | BF | slides record | |
| 20.04.2022 | 10. Learning Representations I: Metric Learning, Word Vectors | AS | slides record | |
| 27.04.2022 | 11. Learning Representations II: t-SNE, Stochastic EM | AS | slides part 1 part 2 | |
| 04.05.2022 | 12. Learning Representations III: Variational Autoencoders | BF | slides record | D. Kingma, M. Welling (2019), An Introduction to VAEs |
| 11.05.2022 | — Rector's day — | |||
| 18.05.2022 | 13. Recurrent Neural Networks. Recurrent Back-Propagation | BF | slides record |