263-2400-00L Reliable and Trustworthy Artificial Intelligence
Semester | Herbstsemester 2021 |
Dozierende | M. Vechev |
Periodizität | jährlich wiederkehrende Veranstaltung |
Lehrsprache | Englisch |
Lehrveranstaltungen
Nummer | Titel | Umfang | Dozierende | |||||||
---|---|---|---|---|---|---|---|---|---|---|
263-2400-00 V | Reliable 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. |
| M. Vechev | ||||||
263-2400-00 U | Reliable 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. |
| M. Vechev | ||||||
263-2400-00 A | Reliable and Trustworthy Artificial Intelligence | 1 Std. | M. Vechev |
Katalogdaten
Kurzbeschreibung | Creating 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. |
Lernziel | The 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. |
Inhalt | This 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 / Besonderes | 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. |
Leistungskontrolle
Information zur Leistungskontrolle (gültig bis die Lerneinheit neu gelesen wird) | |
Leistungskontrolle als Semesterkurs | |
ECTS Kreditpunkte | 6 KP |
Prüfende | M. Vechev |
Form | Sessionsprüfung |
Prüfungssprache | Englisch |
Repetition | Die Leistungskontrolle wird in jeder Session angeboten. Die Repetition ist ohne erneute Belegung der Lerneinheit möglich. |
Prüfungsmodus | schriftlich 120 Minuten |
Zusatzinformation zum Prüfungsmodus | 30% of your grade is determined by mandatory project work and 70% is determined by a written exam. |
Hilfsmittel schriftlich | Two 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
Hauptlink | Information |
Es werden nur die öffentlichen Lernmaterialien aufgeführt. |
Gruppen
Keine Informationen zu Gruppen vorhanden. |
Einschränkungen
Keine zusätzlichen Belegungseinschränkungen vorhanden. |