Labs
| Datum | Č.T. | S/L | Náplň | Učitel | Materiály | Úkol |
|---|---|---|---|---|---|---|
| 22.-26.9.2025 | 1 | L | Intro: Introduction to the course and machine learning. | Neumann L. | ||
| 29.9-3.10.2025 | 2 | S | 1D regression and 2D classification: Revision of the regression and classification theory, analytic gradient computation, gradient in computational graph and loss minimization. | Pimenova O. | template_for_students.zip | |
| 6.-10.10.2025 | 3 | L | Loss, MLP | Kučera A. | HW01 - MLP | |
| 13.-17.10.2025 | 4 | S | Autograd: Computational graphs, backpropagation and the automatic gradient computation. | Vlk J. | slides lab04.zip lab04_ref.zip | |
| 20.-24.10.2025 | 5 | L | CNN: Introduction to the convolution and convolution neural network. | Vlk J. | lab05.zip lab05_ref.zip | HW02 - Autograd |
| 27.-31.10.2025 | 6 | S | Training of neural networks | Hlavsa J. | lab06_2025.zip lab06_2025_solved.zip | HW03 - segmentation |
| 3.-7.11.2025 | 7 | L | Layers of Neural Networks | Hlavsa J. | lab7_2025.zip | |
| 10-14.11.2025 | 8 | S | Optimization: Convergence rate, oscillations, diminishing gradients. | Pimenova O. | optimizers_student_template.py.zip    | |
| 17.-21.11.2025 | 9 | L | Holiday | - | - | - |
| 23.-28.11.2025 | 10 | S | Transformers I: Introduction to the transformer architecture, GPT2 | Čapek D. | lab10.zip lab10_ref.zip | |
| 3.-5.12.2025 | 11 | L | Transformers II: Intro into ViTs | Čapek D. | HW04 - Transformers | |
| 8.-12.12.2025 | 12 | S | RL I Intro: Introduction to the reinforcement learning. Policy gradient. | Mrkos M. | https://urob-ctu.github.io/docs/docs/reinforcement_learning/reinforcement_learning.html | |
| 15.-19.12.2025 | 13 | L | RL II - Deep learning: Deep reinforcement learning. | Mrkos M. | HW05 - RL | |
| 5.-9.1.2025 | 14 | S |