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	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