====== Lectures ======

^ 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  | <fc #f00000>// Public holiday //</fc> | |
| 04.11.2026 | 7  | **Midterm test** | {{ :courses:b3b33urob:lectures:vir_2022_midterm_test.pdf |}} \\ {{ :courses:b3b33urob:lectures:vir_2022_midterm_solution.pdf |}} \\ {{ :courses:b3b33urob:lectures:vir_2022_training_questions_midterm_test.pdf |}} \\ {{ :courses:becm33dpl:lectures: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** | {{ :courses:b3b33urob:lectures:exam_vir_2022.pdf |}}\\ {{ :courses:b3b33urob:lectures:vir_2022_training_questions_exam_test.pdf |}} \\ {{ :courses:b3b33urob:lectures:exam_2021.pdf |}} \\ {{ :courses:b3b33urob:lectures:exam_2022.pdf |}} |