Fairness and Privacy in Machine Learning
This course gives a thorough technical introduction to algorithmic privacy and fairness. The course work is centered around individual assignments and group project work.
- Introduction: algorithmic privacy, fairness and reproducibility.
- Privacy and anonymity
- Differential privacy
- Approximate differential privacy
- Privacy amplification
- Group fairness: Equalised odds
- Group fairness: Balance and calibration
- Individual fairness: Meritocracy
- Individual fairness: Smoothness
- Reproducibility
Details
| Code | 42116 62116 |
| Type | Course |
| ECTS | 5 |
| Site | Neuchâtel |
| Track(s) |
T4 – Theory and Logic T6 – Data Science |
| Semester | A2023 |
Teaching
| Lecturer(s) |
Christos Dimitrakakis |
| Language | english |
| Course Page | The course page in ILIAS can be found at https://ilias.unibe.ch/goto_ilias3_unibe_crs_2793349.html. |
Schedules and Rooms
| Period | Weekly |
| Schedule | Friday, 08:45 - 12:00 |
| Location | UniNE, Unimail |
| Room | E213 |
Additional information
| Comment | First Lecture
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