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.

  1. Introduction: algorithmic privacy, fairness and reproducibility.
  2. Privacy and anonymity
  3. Differential privacy
  4. Approximate differential privacy
  5. Privacy amplification
  6. Group fairness: Equalised odds
  7. Group fairness: Balance and calibration
  8. Individual fairness: Meritocracy
  9. Individual fairness: Smoothness
  10. Reproducibility


Code 62116
Type Course
Site Neuchâtel
Track(s) T6 – Data Science
Semester A2022


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_2469224.html.

Schedules and Rooms

Period Weekly
Schedule Tuesday, 08:15 - 12:00
Location UniNE, Unimail
Room B013


Evaluation type written exam

Additional information


First Lecture
The first lecture will take place on Tuesday, 27.09.2022 at 08:50 in UniNE, Unimail, room B013.