Algorithmen und Datenstructuren (Service)
Semester:
SummerSprache:
GermanCredits:
4-6Kontakt:
alds@combi.rwth-aachen.deReguläre Studiengänge:
- Physics (& Physik Plus) B.Sc.
- Applied Geography B.Sc., M.Sc.
- Cognitive, Digital and Empirical English Studies M.Sc.
- Economic Geography M.Sc.
Klausur:
- Prüfungsart: Mündlich oder Schriftlich
- Prüfungsvoraussetzungen: Refer to the module handbook
Team
buesing@combi.rwth-aachen.de
- Robust Optimization
- Combinatorial Optimization
- Healthcare Applications
mathwieser@combi.rwth-aachen.de
- Explorable Uncertainty
- Colored Matching Problems
- Mobile Healthcare Services
Inhalt
Lernziele
- Students know basic data structures. Students know fundamental (graph) algorithms for a selection of basic problems in computer science.
- Students can model simple problems from practice mathematically and/or graph-theoretically and identify suitable algorithms.
- Students can classify simple complexity theory statements, distinguish between exponential and polynomial runtime and recognise when the choice of a correct data structure has a significant influence on the performance of an algorithm.
- Students can use (already known) results on the runtime and memory requirements of an algorithm to assess its suitability for practical problems.
- Students can make simple modifications to standard algorithms in order to tailor them to specific problems.
- Students can read theoretical computer science texts and comprehend unknown proofs.
- Students select suitable algorithms and data structures for problems for which standard implementations are inadequate. If necessary, they can design simple algorithms themselves using existing principles.
Empfohlene Vorkenntnisse
- Basic knowledge of programming (e.g., „Introduction to programming for Data-driven science“)
- Basic knowledge of mathematical notation and proofs (e.g., „Higher Mathematics“ or „Analysis“)