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.
Courses
Number
Title
Hours
Lecturers
263-5156-00 S
Beyond iid Learning: Causality, Dynamics, and Interactions
The lecturers will communicate the exact lesson times of ONLINE courses.
Many machine learning problems go beyond supervised learning on independent data points and require an understanding of the underlying causal mechanisms, the interactions between the learning algorithms and their environment, and adaptation to temporal changes. The course highlights some of these challenges and relates them to state-of-the-art research.
Learning objective
The goal of this seminar is to gain experience with machine learning research and foster interdisciplinary thinking.
Content
The seminar will be divided into two parts. The first part summarizes the basics of statistical learning theory, game theory, causal inference, and dynamical systems in four lectures. This sets the stage for the second part, where distinguished speakers will present selected aspects in greater detail and link them to their current research.
Keywords: Causal inference, adaptive decision-making, reinforcement learning, game theory, meta learning, interactions with humans.
BSc in computer science or related field (engineering, physics, mathematics). Passed at least one learning course, such as ``Introduction to Machine Learning" or ``Probabilistic Artificial Intelligence".
Performance assessment
Performance assessment information (valid until the course unit is held again)
Registration for the course unit is until 04.10.2021 only possible for the primary target group
Primary target group
Robotics, Systems and Control MSc (159000)
Electrical Engin. + Information Technology MSc (237000)
Data Science MSc (261000)
Computer Science MSc (263000)
CAS ETH in Computer Science (269000)
Statistics MSc (436000)
Performance assessment as a semester course