263-2400-00L  Reliable and Trustworthy Artificial Intelligence

SemesterHerbstsemester 2021
DozierendeM. Vechev
Periodizitätjährlich wiederkehrende Veranstaltung
LehrspracheEnglisch



Lehrveranstaltungen

NummerTitelUmfangDozierende
263-2400-00 VReliable and Trustworthy Artificial Intelligence
Online event: Will primarily take place online. Reserved rooms will remain blocked on campus for students to follow the course from there.
2 Std.
Mi14-16HG G 3 »
M. Vechev
263-2400-00 UReliable and Trustworthy Artificial Intelligence
Exercise session will start in the second week of the semester.
Online event: Will primarily take place online. Reserved rooms will remain blocked on campus for students to follow the course from there.
2 Std.
Mo12-14CAB G 56 »
Mi12-14CAB G 51 »
M. Vechev
263-2400-00 AReliable and Trustworthy Artificial Intelligence1 Std.M. Vechev

Katalogdaten

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.
InhaltThis comprehensive course covers some of the latest and most important research advances (over the last 3 years) underlying the creation of safe, trustworthy, and reliable AI (more information here: https://www.sri.inf.ethz.ch/teaching/reliableai21):

* 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 relaxations, mixed-integer solvers).
* Probabilistic certification of deep neural networks
* Training deep neural networks to be provably robust via automated reasoning
* Fairness (different notions of fairness, certifiably fair representation learning)
* Federated Learning (introduction, security considerations)
Voraussetzungen / BesonderesWhile 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.

Leistungskontrolle

Information zur Leistungskontrolle (gültig bis die Lerneinheit neu gelesen wird)
Leistungskontrolle als Semesterkurs
ECTS Kreditpunkte6 KP
PrüfendeM. Vechev
FormSessionsprüfung
PrüfungsspracheEnglisch
RepetitionDie Leistungskontrolle wird in jeder Session angeboten. Die Repetition ist ohne erneute Belegung der Lerneinheit möglich.
Prüfungsmodusschriftlich 120 Minuten
Zusatzinformation zum Prüfungsmodus30% of your grade is determined by mandatory project work and 70% is determined by a written exam.
Hilfsmittel schriftlichTwo A4-pages (i.e. one two-sided or two one-sided A4-sheets of paper), either handwritten or 11 point minimum font size.
Diese Angaben können noch zu Semesterbeginn aktualisiert werden; verbindlich sind die Angaben auf dem Prüfungsplan.

Lernmaterialien

 
HauptlinkInformation
Es werden nur die öffentlichen Lernmaterialien aufgeführt.

Gruppen

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Einschränkungen

Keine zusätzlichen Belegungseinschränkungen vorhanden.

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