Martin Vechev: Catalogue data in Autumn Semester 2022
|Name||Prof. Dr. Martin Vechev|
Inst. Programmiersprachen u. -syst
ETH Zürich, CAB H 69.1
|Telephone||+41 44 632 98 48|
|252-2600-05L||Software Engineering Seminar |
Number of participants limited to 22.
The deadline for deregistering expires at the end of the second week of the semester. Students who are still registered after that date, but do not attend the seminar, will officially fail the seminar.
|2 credits||2S||Z. Su, M. Vechev|
|Abstract||The course is an introduction to research in software engineering, based on reading and presenting high quality research papers in the field. The instructor may choose a variety of topics or one topic that is explored through several papers.|
|Objective||The main goals of this seminar are 1) learning how to read and understand a recent research paper in computer science; and 2) learning how to present a technical topic in computer science to an audience of peers.|
|Content||The technical content of this course falls into the general area of software engineering but will vary from semester to semester.|
|263-2400-00L||Reliable and Trustworthy Artificial Intelligence||6 credits||2V + 2U + 1A||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.