Martin Vechev: Katalogdaten im Herbstsemester 2020

NameHerr Prof. Dr. Martin Vechev
LehrgebietInformatik
Adresse
Inst. Programmiersprachen u. -syst
ETH Zürich, CAB H 69.1
Universitätstrasse 6
8092 Zürich
SWITZERLAND
Telefon+41 44 632 98 48
E-Mailmartin.vechev@inf.ethz.ch
URLhttp://www.srl.inf.ethz.ch/
DepartementInformatik
BeziehungOrdentlicher Professor

NummerTitelECTSUmfangDozierende
263-2100-00LResearch Topics in Software Engineering Information Belegung eingeschränkt - Details anzeigen
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 KP2SZ. Su, M. Vechev
KurzbeschreibungThis seminar is an opportunity to become familiar with current research in software engineering and more generally with the methods and challenges of scientific research.
LernzielEach student will be asked to study some papers from the recent software engineering literature and review them. This is an exercise in critical review and analysis. Active participation is required (a presentation of a paper as well as participation in discussions).
InhaltThe aim of this seminar is to introduce students to recent research results in the area of programming languages and software engineering. To accomplish that, students will study and present research papers in the area as well as participate in paper discussions. The papers will span topics in both theory and practice, including papers on program verification, program analysis, testing, programming language design, and development tools.
LiteraturThe publications to be presented will be announced on the seminar home page at least one week before the first session.
Voraussetzungen / BesonderesOrganizational note: the seminar will meet only when there is a scheduled presentation. Please consult the seminar's home page for information.
263-2400-00LReliable and Interpretable Artificial Intelligence Information 6 KP2V + 2U + 1AM. Vechev
KurzbeschreibungCreating reliable and explainable probabilistic models is a fundamental challenge to solving the artificial intelligence problem. This course covers some of the latest and most exciting advances that bring us closer to constructing such models.
LernzielThe main objective of this course is to expose students to the latest and most exciting research in the area of explainable and interpretable artificial intelligence, a topic of fundamental and increasing importance. Upon completion of the course, the students should have mastered the underlying methods and be able to apply them to a variety of problems.

To facilitate deeper understanding, an important part of the course will be a group hands-on programming project where students will build a system based on the learned material.
InhaltThe course covers some of the latest research (over the last 2-3 years) underlying the creation of safe, trustworthy, and reliable AI (more information here: https://www.sri.inf.ethz.ch/teaching/riai2020):

* Adversarial Attacks on Deep Learning (noise-based, geometry attacks, sound attacks, physical attacks, autonomous driving, out-of-distribution)
* Defenses against attacks
* Combining gradient-based optimization with logic for encoding background knowledge
* Complete Certification of deep neural networks via automated reasoning (e.g., via numerical abstractions, mixed-integer solvers).
* Probabilistic certification of deep neural networks
* Training deep neural networks to be provably robust via automated reasoning
* Understanding and Interpreting Deep Networks
* Probabilistic Programming
Voraussetzungen / BesonderesWhile not a formal requirement, the course assumes familiarity with basics of machine learning (especially probability theory, linear algebra, gradient descent, and neural networks). 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 excepted.