263-2400-00L  Reliable and Trustworthy Artificial Intelligence

SemesterAutumn Semester 2022
LecturersM. Vechev
Periodicityyearly recurring course
Language of instructionEnglish


263-2400-00 VReliable and Trustworthy Artificial Intelligence2 hrs
Wed14:15-16:00HG G 3 »
M. Vechev
263-2400-00 UReliable and Trustworthy Artificial Intelligence
Exercise session will start in the second week of the semester.
2 hrs
Mon12:15-14:00CAB G 56 »
Wed12:15-14:00CAB G 51 »
M. Vechev
263-2400-00 AReliable and Trustworthy Artificial Intelligence1 hrsM. Vechev

Catalogue data

AbstractCreating 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.
ObjectiveUpon 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.
ContentThe 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.
Prerequisites / NoticeWhile 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.
Fostered competenciesFostered competencies
Subject-specific CompetenciesConcepts and Theoriesassessed
Techniques and Technologiesassessed
Method-specific CompetenciesAnalytical Competenciesassessed
Personal CompetenciesCreative Thinkingassessed
Critical Thinkingassessed

Performance assessment

Performance assessment information (valid until the course unit is held again)
Performance assessment as a semester course
ECTS credits6 credits
ExaminersM. Vechev
Typesession examination
Language of examinationEnglish
RepetitionThe performance assessment is only offered in the session after the course unit. Repetition only possible after re-enrolling.
Mode of examinationwritten 120 minutes
Additional information on mode of examination30% 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.
Written aidsTwo A4-pages (i.e. one two-sided or two one-sided A4-sheets of paper), either handwritten or 11 point minimum font size.
This information can be updated until the beginning of the semester; information on the examination timetable is binding.

Learning materials

Main linkInformation
Only public learning materials are listed.


No information on groups available.


There are no additional restrictions for the registration.

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