| Date | Week | Title | Resources |
|---|---|---|---|
| 23.09.2026 | 1 | Machine learning 101: engineering view on models, loss, learning, learning issues, regression, classification | |
| 30.09.2026 | 2 | Linear classifier: two-class and multi-class linear classifier on RGB images | |
| 07.10.2026 | 3 | Maximum Likelihood Estimation (MLE), KL Divergence: Where does loss function come from, why overfitting exists? | |
| 14.10.2026 | 4 | Neural Networks: Perceptron, MLP, Backpropagation, Vector-Jacobian product, Autograd | |
| 21.10.2026 | 5 | The story of the cat's brain surgery: cortex + convolutional layer and its Vector-Jacobian-Product (VJP), fun with backpropagation | |
| 28.10.2026 | 6 | Public holiday | |
| 04.11.2026 | 7 | Midterm test | vir_2022_midterm_test.pdf vir_2022_midterm_solution.pdf vir_2022_training_questions_midterm_test.pdf dpl_2025_midterm_test.pdf |
| 11.11.2026 | 8 | Activation, Normalization and Regularization: activation functions, BatchNorm, Dropout, weight decay | |
| 18.11.2025 | 9 | Optimization: SGD, momentum, RMSProp, Adam | |
| 25.11.2025 | 10 | Backbone architectures: ResNet, EfficientNet, Transformers | |
| 02.12.2025 | 11 | Task-specific architectures: Object detection, pose estimation, generative networks | |
| 09.12.2026 | 12 | Reinforcement learning | |
| 16.12.2026 | 13 | Implicit layers | |
| 06.01.2027 | 14 | Final test | exam_vir_2022.pdf vir_2022_training_questions_exam_test.pdf exam_2021.pdf exam_2022.pdf |