| Week | Date | Topic | Point |
|---|---|---|---|
| 1 | Feb 24, 2022 | Introduction to the course | |
| 2 | Mar 3, 2022 | ML Philosophy and XAI + Lab 1 | |
| 3 | Mar 10, 2022 | Break-Down method and Shapley Values + Lab 2 | |
| 4 | Mar 17, 2022 | Overview of homework I | 9 pts. |
| 5 | Mar 24, 2022 | LIME method + Lab 3 | |
| 6 | Mar 31, 2022 | Ceteris Paribus Profiles + Lab 4 | |
| 7 | Apr 7, 2022 | Model performance measures + Lab 5 + Homework II | 9 pts. |
| 8 | Apr 14, 2022 | Variable importance measures + Lab6 | |
| 9 | Apr 21, 2022 | Partial Dependence Profiles + Lab7 + Homework III | 10 pts. |
| 10 | Apr 28, 2022 | Accumulated Local Effects + Lab8 | |
| 11 | May 5, 2022 | Residual-diagnostics plots + Lab9 + Homework IV | 10 pts. |
| 12 | May 19, 2022 | Preliminary project presentation + Homework V | 10 pts. |
| 13 | May 26, 2022 | Writing practices | |
| 14 | Jun 2, 2022 | Project consultation | |
| 15 | Jun 9, 2022 | Summary of the projects |
- Work in laboratories - 48 points
- Recorded presentation - 16 points (evaluated by all lecturers)
- The two class group presentations with the best grades will be presented to all students.
- Final report - 32 points
- Application of good practices of using Git - 4 points.
- Homework I [0-9 points]
- Homework II [0-9 points]
- Homework III [0-10 points]
- Homework IV [0-10 points]
- Homework V [0-10 points]