====== 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. | {{ :courses:b3b33urob:tutorials:template_for_students.zip |}} |  |
| 6.-10.10.2025 | 3  | L | __Loss, MLP__ | Kučera A. |  | [[courses:b3b33urob:tutorials:hw1|HW01 - MLP]] |
| 13.-17.10.2025 | 4  | S | __Autograd:__ Computational graphs, backpropagation and the automatic gradient computation. | Vlk J. |  |
| 20.-24.10.2025 | 5  | L | __CNN:__ Introduction to the convolution and convolution neural network. | Vlk J. |  | [[courses:b3b33urob:tutorials:hw2|HW02 - Autograd]] |
| 27.-31.10.2025 | 6  | S | __Training of neural networks__ | Hlavsa J. |  |
| 3.-7.11.2025 | 7  | L | __Layers of Neural Networks__ | Hlavsa J. |  | [[courses:b3b33urob:tutorials:hw3|HW03 - segmentation]] |
| 10-14.11.2025 | 8  | S | __Optimization:__ Convergence rate, oscillations, diminishing gradients. | Pimenova O. |  |  |
| 17.-21.11.2025 | 9  | L | Holiday | - | - | - |
| 23.-28.11.2025 | 10 | S |  __Transformers I:__  Introduction to the transformer architecture, GPT2 | Čapek D. |  |  |
| 3.-5.12.2025 | 11 | L | __Transformers II:__ Intro into ViTs| Čapek D. | | [[courses:b3b33urob:tutorials:hw4|HW04 - Transformers]] |
| 8.-12.12.2025 | 12 | S | __RL I Intro:__ Introduction to the reinforcement learning. Policy gradient. | Mrkos M. |  |  |
| 15.-19.12.2025 | 13 | L |  __RL II - Deep learning:__ Deep reinforcement learning. | Mrkos M. | | [[courses:b3b33urob:tutorials:hw5|HW05 - RL]] |
| 5.-9.1.2025 | 14 | S | | | | |