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
T6 – Data Science |
Schedules and Rooms
||Tuesday, 08:15 - 12:00|
The first lecture will take place on Tuesday, 27.09.2022 at 08:50 in UniNE, Unimail, room B013.