Labs and 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. Additionally, some assignments will involve writing a report that includes responses to the given problems. These reports will be evaluated manually.

  • All homeworks must be submitted through the upload system.
  • All code must be implemented in Python.
  • Deadlines will be set for the submissions.

Lab/Seminar plan

Week Date Topic Teacher Materials Notes / Homeworks Deadline
1 25. 9. seminar cancelled
2 2. 10. Seminar: Predictor Evaluation VF/JP (with solutions) Assignment of HW 1 6.11.2025
3 9. 10. Seminar: Probably Approximately Correct Learning VF/JP (with solutions) HW 2 13.11.2025
4 16. 10. Seminar: VC dimension VF/JP (with solutions)
5 23. 10. Seminar: Neural Networks JD/JP
6 30. 10. seminar cancelled
7 6. 11. Lab: Neural Networks JD
8 13. 11. Seminar: Support Vector Machines VF/JP
9 20. 11. Seminar: Ensembling JD/JP
10 27. 11. Seminar: Gradient Boosting Machine JD/JP
11 4. 12. Seminar: Generative learning, Maximum Likelihood estimator VF/JP
12 11. 12. Seminar: EM algoritm; Bayesian learning VF
13 18. 12. Seminar: (Hidden) Markov Models JD/JP
14 8. 1. reserved
courses/be4m33ssu/labs.txt · Last modified: 2025/10/21 10:25 by drchajan