David Steurer: Catalogue data in Spring Semester 2023 |
| Name | Prof. Dr. David Steurer |
| Field | Theoretical Computer Science |
| Address | Professur Theoretische Informatik ETH Zürich, OAT Z 22.2 Andreasstrasse 5 8092 Zürich SWITZERLAND |
| david.steurer@inf.ethz.ch | |
| URL | https://www.dsteurer.org |
| Department | Computer Science |
| Relationship | Associate Professor |
| Number | Title | ECTS | Hours | Lecturers | |
|---|---|---|---|---|---|
| 252-4202-00L | Seminar in Theoretical Computer Science  | 2 credits | 2S | E. Welzl, B. Gärtner, M. Hoffmann, J. Lengler, A. Steger, D. Steurer, B. Sudakov | |
| Abstract | Presentation of recent publications in theoretical computer science, including results by diploma, masters and doctoral candidates. | ||||
| Learning objective | To get an overview of current research in the areas covered by the involved research groups. To present results from the literature. | ||||
| Prerequisites / Notice | This seminar takes place as part of the joint research seminar of several theory groups. Intended participation is for students with excellent performance only. Formal restriction is: prior successful participation in a master level seminar in theoretical computer science. | ||||
| 252-4225-00L | Presenting Theoretical Computer Science  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 credits | 2S | B. Gärtner, D. Komm, R. Kyng, A. Steger, D. Steurer, E. Welzl | |
| Abstract | Students present current or classical results from theoretical computer science. | ||||
| Learning objective | Students learn to read, understand and present results from theoretical computer science. The main focus and deliverable is a good presentation of 45 minutes that can easily be followed and understood by the audience. | ||||
| Content | Students present current or classical results from theoretical computer science. | ||||
| Prerequisites / Notice | The seminar takes place as a block seminar on two Saturdays in April and/or May. Each presentation is jointly prepared and given by two students (procedure according to the seminar's Moodle page). All students must attend all presentations. Participation requires successful completion of the first year, or instructor approval. | ||||
| 263-4508-00L | Algorithmic Foundations of Data Science | 10 credits | 3V + 2U + 4A | D. Steurer | |
| Abstract | This course provides rigorous theoretical foundations for the design and mathematical analysis of efficient algorithms that can solve fundamental tasks relevant to data science. | ||||
| Learning objective | We consider various statistical models for basic data-analytical tasks, e.g., (sparse) linear regression, principal component analysis, matrix completion, community detection, and clustering. Our goal is to design efficient (polynomial-time) algorithms that achieve the strongest possible (statistical) guarantees for these models. Toward this goal we learn about a wide range of mathematical techniques from convex optimization, linear algebra (especially, spectral theory and tensors), and high-dimensional statistics. We also incorporate adversarial (worst-case) components into our models as a way to reason about robustness guarantees for the algorithms we design. | ||||
| Content | Strengths and limitations of efficient algorithms in (robust) statistical models for the following (tentative) list of data analysis tasks: - (sparse) linear regression - principal component analysis and matrix completion - clustering and Gaussian mixture models - community detection | ||||
| Lecture notes | To be provided during the semester | ||||
| Literature | High-Dimensional Statistics A Non-Asymptotic Viewpoint by Martin J. Wainwright | ||||
| Prerequisites / Notice | Mathematical and algorithmic maturity at least at the level of the course "Algorithms, Probability, and Computing". Important: Optimization for Data Science 2018--2021 This course was created after a reorganization of the course "Optimization for Data Science" (ODS). A significant portion of the material for this course has previously been taught as part of ODS. Consequently, it is not possible to earn credit points for both this course and ODS as offered in 2018--2021. This restriction does not apply to ODS offered in 2022 or afterwards and you can earn credit points for both courses in this case. | ||||