263-2400-00L  Reliable and Trustworthy Artificial Intelligence

SemesterAutumn Semester 2021
LecturersM. Vechev
Periodicityyearly recurring course
Language of instructionEnglish



Courses

NumberTitleHoursLecturers
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 hrs
Wed14-16HG 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 hrs
Mon12-14CAB G 56 »
Wed12-14CAB G 51 »
M. Vechev
263-2400-00 AReliable and Trustworthy Artificial Intelligence1 hrsM. Vechev

Catalogue data

AbstractCreating 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.
ObjectiveThe 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.
ContentThis 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)
Prerequisites / NoticeWhile 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

Performance assessment information (valid until the course unit is held again)
Performance assessment as a semester course
ECTS credits6 credits
ExaminersM. Vechev
Typesession examination
Language of examinationEnglish
RepetitionThe performance assessment is offered every session. Repetition possible without re-enrolling for the course unit.
Mode of examinationwritten 120 minutes
Additional information on mode of examination30% of your grade is determined by mandatory project work and 70% is determined by a written exam.
Written aidsTwo 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.

Learning materials

 
Main linkInformation
Only public learning materials are listed.

Groups

No information on groups available.

Restrictions

There are no additional restrictions for the registration.

Offered in

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