Seminars

In the seminar, we will discuss solutions to theoretical assignments. These assignments will be made available in advance, and students are encouraged to work on them beforehand.

During the seminars, we will also assign and explain the practical homework tasks. Students will be required to implement solutions and submit their code for automatic evaluation.

  • All homeworks must be submitted through the upload system.
  • All code must be implemented in Python.
  • Deadlines will be set for the submissions. There will be no deadline extensions.

Lab/Seminar plan

Week Date Topic Teacher Materials Notes / Homeworks Deadline
1 17. 2. Introduction VF Assignments+solutions
2 24. 2. Predictor evaluation VF Assignments+solutions Homework 24.03.2026
3 3. 3. Empirical Risk Minimization VF Assignments+solutions
4 10. 3. PAC learning VF Assignments+solutions Homework 07.04.2026
5 17.3. VC dimension VF Assignments+solutions
6 24.3. Generative Learning VF Assignments+solutions Homework 21.04.2026
7 31.3 Linear models VF Assignments+solutions
8 7.4 Kernel functions VF Assignments+solutions
9 14.4. Support Vector Machines VF Assignments+solutions
10 20.4. Model selection VF Homework 22.05.2026
11 28.4. Unsupervised learning VF Assignments+solutions
12 5.5. Bayesian learning VF Assignments+solutions
13 12.5. Deep Learning VF Assignments+solutions
courses/becm33mlf/labs.txt · Last modified: 2026/05/12 18:04 by xfrancv