Lectures

Date	Week	Title	Resources
24.09.2025	1	Machine learning 101: engineering view on models, loss, learning, learning issues, regression, classification	01_machinelearning101.pdf
01.10.2025	2	Linear classifier: two-class and multi-class linear classifier on RGB images	02_linearclassifier.pdf
08.10.2025	3	Maximum Likelihood Estimation (MLE), KL Divergence: Where does loss function come from, why overfitting exists?	03_maximumlikelihoodkldivergence.pdf
15.10.2025	4	Neural Networks: Perceptron, MLP, Backpropagation, Vector-Jacobian product, Autograd	04_neuralnetworks.pdf
22.10.2025	5	Lecture cancelled (ICCV 2025)
29.10.2025	6	The story of the cat's brain surgery: cortex + convolutional layer and its Vector-Jacobian-Product (VJP), fun with backpropagation	05_cnns.pdf
05.11.2024	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 Deep learning and Halloween:
12.11.2025	8	Activation, Normalization and Regularization: activation functions, BatchNorm, Dropout, weight decay	06_normalization.pdf
19.11.2025	9	Optimization: SGD, momentum, RMSProp, Adam	07_optimization.pdf
26.11.2025	10	Backbone architectures: ResNet, EfficientNet, Transformers	08_backbonearchitectures.pdf
03.12.2025	11	Task-specific architectures: Object detection, pose estimation, generative networks	09_taskspecificarchitectures.pdf
10.12.2025	12	Reinforcement learning	10_reinforcementlearning.pdf
17.12.2025	13	Implicit layers	11_implicitlayers.pdf
07.01.2026	14	Final test	exam_vir_2022.pdf vir_2022_training_questions_exam_test.pdf exam_2021.pdf exam_2022.pdf