263-2400-00L  Reliable and Trustworthy Artificial Intelligence

SemesterHerbstsemester 2022
DozierendeM. Vechev
Periodizitätjährlich wiederkehrende Veranstaltung


263-2400-00 VReliable and Trustworthy Artificial Intelligence2 Std.
Mi14:15-16:00HG G 3 »
M. Vechev
263-2400-00 UReliable and Trustworthy Artificial Intelligence
Exercise session will start in the second week of the semester.
2 Std.
Mo12:15-14:00CAB G 56 »
Mi12:15-14:00CAB G 51 »
M. Vechev
263-2400-00 AReliable and Trustworthy Artificial Intelligence1 Std.M. Vechev


KurzbeschreibungCreating reliable, secure, robust, and fair machine learning models is a core challenge in artificial intelligence and one of fundamental importance. The goal of the course is to teach both the mathematical foundations of this new and emerging area as well as to introduce students to the latest and most exciting research in the space.
LernzielUpon completion of the course, the students should have mastered the underlying methods and be able to apply them to a variety of engineering and research problems. To facilitate deeper understanding, the course includes a group coding project where students will build a system based on the learned material.
InhaltThe course is split into 3 parts:

Robustness in Deep Learning

- Adversarial attacks and defenses on deep learning models.
- Automated certification of deep learning models (covering the major trends: convex relaxations and branch-and-bound methods as well as randomized smoothing).
- Certified training of deep neural networks to satisfy given properties (combining symbolic and continuous methods).

Privacy of Machine Learning

- Threat models (e.g., stealing data, poisoning, membership inference, etc.).
- Attacking federated machine learning (across modalities such as vision, natural language and tabular) .
- Differential privacy for defending machine learning.
- Enforcing regulations with guarantees (e.g., via provable data minimization).

Fairness of Machine Learning

- Introduction to fairness (motivation, definitions).
- Enforcing individual fairness with guarantees (e.g., for both vision or tabular data).
- Enforcing group fairness with guarantees.

More information here: Link.
Voraussetzungen / BesonderesWhile not a formal requirement, the course assumes familiarity with basics of machine learning (especially linear algebra, gradient descent, and neural networks as well as basic probability theory). These topics are usually covered in “Intro to ML” classes at most institutions (e.g., “Introduction to Machine Learning” at ETH).

For solving assignments, some programming experience in Python is expected.
Fachspezifische KompetenzenKonzepte und Theoriengeprüft
Verfahren und Technologiengeprüft
Methodenspezifische KompetenzenAnalytische Kompetenzengeprüft
Persönliche KompetenzenKreatives Denkengeprüft
Kritisches Denkengeprüft


Information zur Leistungskontrolle (gültig bis die Lerneinheit neu gelesen wird)
Leistungskontrolle als Semesterkurs
ECTS Kreditpunkte6 KP
PrüfendeM. Vechev
RepetitionDie Leistungskontrolle wird nur in der Session nach der Lerneinheit angeboten. Die Repetition ist nur nach erneuter Belegung möglich.
Prüfungsmodusschriftlich 120 Minuten
Zusatzinformation zum Prüfungsmodus30% of your grade is determined by mandatory project work and 70% is determined by a written exam.

Students who are repeating the course are required to repeat the project work.
Hilfsmittel schriftlichTwo A4-pages (i.e. one two-sided or two one-sided A4-sheets of paper), either handwritten or 11 point minimum font size.
Diese Angaben können noch zu Semesterbeginn aktualisiert werden; verbindlich sind die Angaben auf dem Prüfungsplan.


Es werden nur die öffentlichen Lernmaterialien aufgeführt.


Keine Informationen zu Gruppen vorhanden.


Keine zusätzlichen Belegungseinschränkungen vorhanden.

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