[Course description] [Announcements] [Lectures] [Assignments] [Reading material]
Instructor: Robert Krauthgamer
When and where: Mondays 14:15-16:00, Ziskind Lecture Room
First class: March 23, 2020
FGS code: 20204182
Syllabus: The course will cover algorithms whose running
time and/or space requirement is sublinear in the input size. Such
algorithms can view only a small portion of the entire input, but
they are particularly suitable for analyzing massive data sets. In
recent years, this approach has been successfully used in several
areas, including graph theory, linear algebra, combinatorial
optimization, geometry, and string matching.
Prerequisites: Basic knowledge of algorithms, probability
and linear algebra at an undergraduate level.
Final: There will be a take-home exam.