The spring semester 2021 will take place online until further notice. Exceptions: Courses that can only be carried out with on-site presence. Please note the information provided by the lecturers.

David Steurer: Catalogue data in Spring Semester 2019

Name Prof. Dr. David Steurer
FieldTheoretical Computer Science
Professur Theoretische Informatik
ETH Zürich, CAB H 37.1
Universitätstrasse 6
8092 Zürich
DepartmentComputer Science
RelationshipAssociate Professor

252-4202-00LSeminar in Theoretical Computer Science Information
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 credits2SA. Steger, B. Gärtner, M. Ghaffari, M. Hoffmann, J. Lengler, D. Steurer, B. Sudakov
AbstractPresentation of recent publications in theoretical computer science, including results by diploma, masters and doctoral candidates.
ObjectiveTo get an overview of current research in the areas covered by the involved research groups. To present results from the literature.
Prerequisites / NoticeThis seminar takes place as part of the joint research seminar of several theory groups. Intended participation is for students with excellent performance only. Formal minimal requirement is passing of one of the courses Algorithms, Probability, and Computing, Randomized Algorithms and Probabilistic Methods, Geometry: Combinatorics and Algorithms, Advanced Algorithms. (If you cannot fulfill this restriction, because this is your first term at ETH, but you believe that you satisfy equivalent criteria, please send an email with a detailed description of your reasoning to the organizers of the seminar.)
261-5110-00LOptimization for Data Science Information 8 credits3V + 2U + 2AB. Gärtner, D. Steurer
AbstractThis course teaches an overview of modern optimization methods, with applications in particular for machine learning and data science.
ObjectiveUnderstanding the theoretical and practical aspects of relevant optimization methods used in data science. Learning general paradigms to deal with optimization problems arising in data science.
ContentThis course teaches an overview of modern optimization methods, with applications in particular for machine learning and data science.

In the first part of the course, we will discuss how classical first and second order methods such as gradient descent and Newton's method can be adapated to scale to large datasets, in theory and in practice. We also cover some new algorithms and paradigms that have been developed specifically in the context of data science. The emphasis is not so much on the application of these methods (many of which are covered in other courses), but on understanding and analyzing the methods themselves.

In the second part, we discuss convex programming relaxations as a powerful and versatile paradigm for designing efficient algorithms to solve computational problems arising in data science. We will learn about this paradigm and develop a unified perspective on it through the lens of the sum-of-squares semidefinite programming hierarchy. As applications, we are discussing non-negative matrix factorization, compressed sensing and sparse linear regression, matrix completion and phase retrieval, as well as robust estimation.
Prerequisites / NoticeAs background, we require material taught in the course "252-0209-00L Algorithms, Probability, and Computing". It is not necessary that participants have actually taken the course, but they should be prepared to catch up if necessary.
263-4110-00LInterdisciplinary Algorithms Lab Restricted registration - show details
Does not take place this semester.
In the Master Programme max. 10 credits can be accounted by Labs on top of the Interfocus Courses. Additional Labs will be listed on the Addendum.
5 credits2PA. Steger, D. Steurer
AbstractIn this course students will develop solutions for algorithmic problems posed by researchers from other fields.
ObjectiveStudents will learn that in order to tackle algorithmic problems from an interdisciplinary or applied context one needs to combine a solid understanding of algorithmic methodology with insights into the problem at hand to judge which side constraints are essential and which can be loosened.
Prerequisites / NoticeStudents will work in teams. Ideally, skills of team members complement each other.

Interested Bachelor students can apply for participation by sending an email to explaining motivation and transcripts.