===== Labs and Seminars =====
Two types of tutorial classes will be proposed for the course:
  * practical labs in which we explain and discuss practical homework tasks. Students will implement selected methods discussed in the course and experiment with them.
  * seminars in which we discuss solutions of theoretical assignments (published a week before the class). Students are expected to work on them in advance.

The solutions of the practical labs have to be submitted using the [[http://cw.felk.cvut.cz/upload/|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.
  * Please notice that you have to submit your code and your report (PDF). The homework will be assessed with zero points if any of the two is missing. 

===== Lab/Seminar plan =====

^Week ^Date ^Topic ^Teacher ^Materials ^Deadline/Notes ^
|  1  | 22. 9. | **Seminar** | BF| {{:courses:be4m33ssu:sem_intro_examples.pdf| }}| no assignments|
|  2  | 29. 9. | **Seminar: lecture 1** | VF/DB | {{:courses:be4m33ssu:seminar_1.pdf| }} |
|  3  | 6. 10. | **Seminar: lecture 2 ** | VF/DB | {{ :courses:be4m33ssu:seminar_2_2022.pdf | }}|
|  4  | 13. 10. | **Seminar: lecture 2,3 ** | VF/DB | {{ :courses:be4m33ssu:seminar_3_ws22.pdf | }} |
|  5  | 20. 10. | **Seminar: lecture 3,4** | VF/DB | {{ :courses:be4m33ssu:seminar_4_ws2022.pdf | }} | |
|  6  | 27. 10. | **Lab: SO Perceptron** | VF/DB | {{ :courses:be4m33ssu:lab_so_perceptron_ws2022.pdf | }} | 25. 11. (extended by one day) |
|  7  | 3. 11.  | **Seminar: Neural Networks** | JD/DB | {{ :courses:be4m33ssu:seminar_5_ws2022.pdf | }} [[https://atmos.washington.edu/~dennis/MatrixCalculus.pdf|Matrix Differentiation]]|
|  8  | 10. 11.| **Lab: Neural Networks** | JD | {{ :courses:be4m33ssu:lab_backprop_ws2022.pdf | }} {{ :courses:be4m33ssu:bp_src.zip |Code}} | 8. 12. |
|  9  | 17. 11. | — National Holiday —  | |  | |
|  10  | 24. 11.| **Seminar: lecture 8** | BF/DB | {{ :courses:be4m33ssu:sem-glearn-mle-ws22.pdf | }} |
|  11  | 1. 12. | **Lab: EM algorithm** | BF/DB |{{ :courses:be4m33ssu:shape_em_binary.pdf | task}} {{ :courses:be4m33ssu:em_data.tgz | data}} | 05. 01. |
|  12  | 8. 12. | **Seminar: lecture 9** | BF/DB | {{ :courses:be4m33ssu:sem-em-bayesian-ws22.pdf | }}|
|  13  | 15. 12. | **Seminar: lecture 10,11** | BF/DB | {{ :courses:be4m33ssu:sem-hmm1-ws22.pdf | }}|  |
|  14  | 12. 1. | **Seminar: Ensembling** | JD/DB | {{ :courses:be4m33ssu:sem_ens_ws2022.pdf | }} |  |