Bernd Gärtner: Catalogue data in Spring Semester 2023

Name	Prof. Dr. Bernd Gärtner
Address	Inst. f. Theoretische Informatik ETH Zürich, OAT Z 15 Andreasstrasse 5 8092 Zürich SWITZERLAND
Telephone	+41 44 632 70 26
Fax	+41 44 632 10 63
E-mail	gaertner@inf.ethz.ch
URL	http://people.inf.ethz.ch/gaertner/
Department	Computer Science
Relationship	Adjunct 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.
261-5110-00L	Optimization for Data Science	10 credits	3V + 2U + 4A	B. Gärtner, N. He
Abstract	This course provides an in-depth theoretical treatment of optimization methods that are relevant in data science.
Learning objective	Understanding the guarantees and limits of relevant optimization methods used in data science. Learning theoretical paradigms and techniques to deal with optimization problems arising in data science.
Content	This course provides an in-depth theoretical treatment of classical and modern optimization methods that are relevant in data science. After a general discussion about the role that optimization has in the process of learning from data, we give an introduction to the theory of (convex) optimization. Based on this, we present and analyze algorithms in the following four categories: first-order methods (gradient and coordinate descent, Frank-Wolfe, subgradient and mirror descent, stochastic and incremental gradient methods); second-order methods (Newton and quasi Newton methods); non-convexity (local convergence, provable global convergence, cone programming, convex relaxations); min-max optimization (extragradient methods). The emphasis is on the motivations and design principles behind the algorithms, on provable performance bounds, and on the mathematical tools and techniques to prove them. The goal is to equip students with a fundamental understanding about why optimization algorithms work, and what their limits are. This understanding will be of help in selecting suitable algorithms in a given application, but providing concrete practical guidance is not our focus.
Prerequisites / Notice	A solid background in analysis and linear algebra; some background in theoretical computer science (computational complexity, analysis of algorithms); the ability to understand and write mathematical proofs.
263-4203-00L	Geometry: Combinatorics and Algorithms 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, M. Hoffmann, E. Welzl, P. Schnider
Abstract	This seminar complements the course Geometry: Combinatorics & Algorithms. Students of the seminar will present original research papers, some classic and some of them very recent.
Learning objective	Each student is expected to read, understand, and elaborate on a selected research paper. To this end, (s)he should give a 45-min. presentation about the paper. The process includes * getting an overview of the related literature; * understanding and working out the background/motivation: why and where are the questions addressed relevant? * understanding the contents of the paper in all details; * selecting parts suitable for the presentation; * presenting the selected parts in such a way that an audience with some basic background in geometry and graph theory can easily understand and appreciate it.
Content	This seminar is held once a year and complements the course Geometry: Combinatorics & Algorithms. Students of the seminar will present original research papers, some classic and some of them very recent. The seminar is a good preparation for a master, diploma, or semester thesis in the area.
Prerequisites / Notice	Prerequisite: Successful participation in the course "Geometry: Combinatorics & Algorithms" (takes place every HS) is required.