HS2022: 62116 Fairness and Privacy in Machine Learning

This course explains the basic concepts of algorithmic privacy and fairness. The main application area is statistics and machine learning.

Allgemeine Informationen

Kursbeschreibung

This course gives a thorough technical introduction to algorithmic privacy and fairness. The course work is centered around individual assignments and group project work.

Assessment: 80% project work, 20% exam

Kursprogramm

1. 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

Zielgruppe

Highly-motivated master students interested in algorithmic privacy and fairness.

Prerequisites, in order of importance:
1. Elementary Probability.
2. Good programming skills.
3. Multivariate calculus
4. Linear algebra

The advanced seminar course explores further topics in the area in more detail. It is possible to take both courses in parallel.

Lernziele und Links

Links

https://github.com/olethrosdc/ml-society-science

Beschreibung

This course explains the basic concepts of algorithmic privacy and fairness. The main application area is statistics and machine learning.

Allgemein

Sprache

Englisch

Kontakt

Name

Christos Dimitrakakis

E-Mail

christos.dimitrakakis@unine.ch

Sprechstunde

By appointment

Verfügbarkeit

Zugriff

Unbegrenzt – wenn online geschaltet

Aufnahmeverfahren

Sie können diesem Kurs direkt beitreten.

Zeitraum für Beitritte

Unbegrenzt

Veranstaltungszeitraum

27. Sep 2022 - 16. Dez 2022

Für Kursadministratoren freigegebene Daten

Daten des Persönlichen Profils

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