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Labs and Seminars

Two types of labs (tutorials) will be proposed for the course (alternating):

  • practical labs in which we explain and discuss practical homework tasks. Students will implement selected methods discussed in the course and experiment with them.
  • theoretical labs in which students will discuss solutions of theoretical assignments (published a week before the class).

The solutions of the practical labs have to be submitted using the upload system

  • Your task will be to program a solution of the assigned problems. You have to hand out your code and a report in PDF. The report has to contain only answers to the assignments (nothing else).
  • As a programming language you can use either Matlab or Python.
  • The deadline for submitting your solutions will be 4 weeks after the date of assignment. This is a hard deadline.

Lab/Seminar plan

Week Date Topic Lecturer Materials Deadline/Notes
1 23. 9. — none —
2 30. 9. Seminar BF
3 7. 10. Seminar: lecture 1,2 VF
4 14. 10. Seminar: lecture 2,3 VF
5 21. 10. Seminar: lecture 3,4 VF/DB
6 28. 10. — National Holiday —
7 4. 11. Lab: SO Perceptron VF/DB ; DP tutorial 2. 12.
8 11. 11. Seminar: Neural Networks JD/DB Matrix Differentiation
9 18. 11. Lab: Neural Networks JD Code 16. 12.
10 25. 11. Seminar: lecture 8 BF/DB
11 2. 12. Lab: EM algorithm BF/DB task data 07. 01.
12 9. 12. Seminar: lecture 9 BF/DB
13 16. 12. Seminar: lecture 10 BF/DB Open learning room: Wed, 15.12., 17:00 KN:G-105
14 6. 1. Seminar: Ensembling JD/DB
courses/be4m33ssu/labs.txt · Last modified: 2021/12/14 12:25 by drchajan