Bernd Gärtner: Katalogdaten im Frühjahrssemester 2018

NameHerr Prof. Dr. Bernd Gärtner
Inst. f. Theoretische Informatik
ETH Zürich, CAB G 31.1
Universitätstrasse 6
8092 Zürich
Telefon+41 44 632 70 26
Fax+41 44 632 10 63

252-4202-00LSeminar in Theoretical Computer Science Information 2 KP2SE. Welzl, B. Gärtner, M. Hoffmann, J. Lengler, A. Steger, B. Sudakov
KurzbeschreibungPresentation of recent publications in theoretical computer science, including results by diploma, masters and doctoral candidates.
LernzielTo get an overview of current research in the areas covered by the involved research groups. To present results from the literature.
252-4220-00LA Taste of Research: Algorithms and Combinatorics Information Belegung eingeschränkt - Details anzeigen
Maximale Teilnehmerzahl: 16

Um das vorhandene Angebot optimal auszunutzen, behält sich das D-INFK vor, Belegungen von Studierenden zu löschen, die sich in mehreren Veranstaltungen dieser Kategorie eingeschrieben haben, bereits die erforderlichen Leistungen in dieser Kategorie erbracht haben oder aus anderen organisatorischen Gründen nicht auf die Belegung der Veranstaltung angewiesen sind.
2 KP2SB. Gärtner, A. Steger, M. Ghaffari
KurzbeschreibungStudents work together with lecturers on open problems in algorithms and combinatorics.
LernzielThe goal is to learn and practice important research techniques: literature search, understanding and presenting research papers, developing ideas in the group, testing of conjectures with the computer, writing down results.
InhaltWork on original research papers and open problems in the areas of algorithms and combinatorics.
SkriptNot available.
LiteraturWill be announced in the seminar.
Voraussetzungen / BesonderesPassed exam in Algorithms, Probability, and Computing.
261-5110-00LOptimization for Data Science Information 8 KP3V + 2U + 2AB. Gärtner, D. Steurer
KurzbeschreibungThis course teaches an overview of modern optimization methods, with applications in particular for machine learning and data science.
LernzielUnderstanding 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.
InhaltThis 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.
Voraussetzungen / BesonderesAs 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.