Labs

Datum	Č.T.	S/L	Náplň	Učitel	Materiály	Úkol
23-25.09.2024	1	L	Intro: Introduction to the course and machine learning.	AK
30.9-02.10.2024	2	S	1D regression and 2D classification: Revision of the regression and classification theory, analytic gradient computation, gradient in computational graph and loss minimization.	KZ	template_for_students.zip labs_02_regression.pdf
07-09.10.2024	3	L	Loss, MLP	AK		HW01 - MLP
14-16.10.2024	4	S	Autograd: Computational graphs, backpropagation and the automatic gradient computation.	JV	lab04.zip lab04.pdf lab04_ref.zip
21-23.10.2024	5	L	CNN: Introduction to the convolution and convolution neural network.	JV	lab05.zip lab05_ref.zip	HW02 - Autograd
28-30.10.2024	6	S	Independence day		Preparation for midterm test
04-06.11.2024	7	L	HPC: High performace computing tutorial.	RS	lab06.zip lab06_ref.zip
11-13.11.2024	8	S	Optimization: Convergence rate, oscillations, diminishing gradients.	KZ	optimizers_student_template.py.zip
18-20.11.2024	9	L	Layers:	RS	hw03.zip	HW03 - segmentation
25-27.11.2024	10	S	Transformers I: Introduction to the transformer architecture, GPT2	DC	lab10.zip lab10_ref.zip
02-04.12.2024	11	L	Transformers II: Intro into ViTs	DC		HW04 - Transformers
09-11.12.2024	12	S	RL I Intro: Introduction to the reinforcement learning. Policy gradient.	DK
16-18.12.2024	13	L	RL II - Deep learning: Deep reinforcement learning.	DK		HW05 - RL
06-08.01.2024	14	S