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Reliable and Trustworthy Artificial Intelligence
Last Updated: 2026-02-05 16:01:51
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 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: https://www.sri.inf.ethz.ch/teaching/rtai22 .
Resources
Learning Materials (Links)
- Main link
- Information
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.
Course Components
| Type | Title | Time & Place | Hours |
|---|---|---|---|
| lecture | Reliable and Trustworthy Artificial Intelligence |
|
2 h weekly |
| exercise |
Reliable and Trustworthy Artificial Intelligence
Exercise session will start in the second week of the semester.
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|
2 h weekly |
| independent project | Reliable and Trustworthy Artificial Intelligence | No time listed | 1 h weekly |
Offered In
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Electives (In the ‘electives’ subcategory, at least two course units must be successfully completed.)
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Statistics Master (The following courses belong to the curriculum of the Master's Programme in Statistics. The corresponding credits do not count as external credits even for course units where an enrolment at ETH Zurich is not possible.)
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