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263-2400-00L 6 Credits MSC , WBZ D-ITET , D-INFK , D-MATH , D-GESS
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Reliable and Trustworthy Artificial Intelligence

Lecturers & Examiners: Prof. Dr. Martin Vechev
VVZ CR 4.0

Last Updated: 2026-02-05 16:30:10

Abstract

Creating 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.

Objective

Upon 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.

Content

The course is split into 4 parts: Robustness of Machine Learning -------------------------------------------- - Adversarial attacks and defenses on deep learning models. - Automated certification of deep learning models (major trends: convex relaxations, branch-and-bound, randomized smoothing). - Certified training of deep neural networks (combining symbolic and continuous methods). Privacy of Machine Learning -------------------------------------- - Threat models (e.g., stealing data, poisoning, membership inference, etc.). - Attacking federated machine learning (across vision, natural language and tabular data). - Differential privacy for defending machine learning. - AI Regulations and checking model compliance. Fairness of Machine Learning --------------------------------------- - Introduction to fairness (motivation, definitions). - Enforcing individual fairness (for both vision and tabular data). - Enforcing group fairness (e.g., demographic parity, equalized odds). Robustness, Privacy and Fairness of Foundation Models --------------------------------------------------------------------------- - We discuss all previous topics, as well as programmability, in the context of latest foundation models (e.g., LLMs). More information here: https://www.sri.inf.ethz.ch/teaching/rtai24 .

Resources

Learning Materials (Links)

General Information

Language
English
Levels
MSC , WBZ
Frequency
Yearly recurring

Examination

Type
session examination
Mode
written 120 minutes
Aids
Two A4-pages (i.e. one two-sided or two one-sided A4-sheets of paper), either handwritten or 11 point minimum font size.
30% 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.

Course Components

Type Title Time & Place Hours
lecture Reliable and Trustworthy Artificial Intelligence
  • Wed 14:15-16:00 (HG G 3)
2 h weekly
exercise Reliable and Trustworthy Artificial Intelligence
Exercise session will start in the second week of the semester.
  • Mon 12:15-14:00 (CAB G 56)
  • Wed 12:15-14:00 (CAB G 51)
2 h weekly
independent project Reliable and Trustworthy Artificial Intelligence No time listed 1 h weekly

Offered In