263-2400-00L Reliable and Trustworthy Artificial Intelligence
|Semester||Autumn Semester 2022|
|Periodicity||yearly recurring course|
|Language of instruction||English|
|263-2400-00 V||Reliable and Trustworthy Artificial Intelligence||2 hrs|
|263-2400-00 U||Reliable and Trustworthy Artificial Intelligence|
Exercise session will start in the second week of the semester.
|263-2400-00 A||Reliable and Trustworthy Artificial Intelligence||1 hrs||M. Vechev|
|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.
|Prerequisites / Notice||While 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.
|Performance assessment information (valid until the course unit is held again)|
|Performance assessment as a semester course|
|ECTS credits||6 credits|
|Language of examination||English|
|Repetition||The performance assessment is only offered in the session after the course unit. Repetition only possible after re-enrolling.|
|Mode of examination||written 120 minutes|
|Additional information on mode of examination||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.
|Written 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.|
|This information can be updated until the beginning of the semester; information on the examination timetable is binding.|
|Only public learning materials are listed.|
|No information on groups available.|
|There are no additional restrictions for the registration.|