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CS566 — Efficient Algorithms
(dt. Effiziente Algorithmen)

Level, degree of commitment Advanced module, compulsory elective module
Forms of teaching and learning,
workload		 Lecture (4 SWS), recitation class (2 SWS),
270 hours (90 h attendance, 180 h private study)
Credit points,
formal requirements		 9 CP
Course requirement(s): Successful completion of at least 50 percent of the points from the weekly exercises as well as at least 2 presentations of the tasks.
Examination type: Oral examination (individual examination) or written examination
Language,
Grading		 German,
The grading is done with 0 to 15 points according to the examination regulations for the degree program B.Sc. Data Science.
Duration,
frequency		 One semester,
each winter semester
Person in charge of the module's outline Prof. Dr. Sebastian Wild

Contents

Translation is missing, sorry. German original:

Algorithmische Methoden

  • Greedy-Verfahren
  • Dynamisches Programmieren
  • Divide-and-Conquer

Laufzeitanalysen (worst-case, amortisiert, ausgabesensitiv, Lösung von Rekurrenzen)

Korrektheitsbeweise

Algorithmen und Datenstrukturen für Mengen, Graphen, Text, geometrische Problemstellungen und Komprimierung


Qualification Goals

Students

  • Are able to apply skills in designing algorithmsand knowledge of key design and analysis paradigms,
  • Are able to use efficient data structures in algorithm design,
  • are able to analyze algorithms with respect to correctness and effort,
  • have practiced scientific working methods (recognizing, formulating, solving problems, abstraction),
  • have trained to speak freely about scientific content, both in front of an audience and in a discussion.

Prerequisites

None. The competences taught in the following modules are recommended: either Algorithms and Data Structures or Practical Informatics II: Data Structures and Algorithms for Pre-Service-Teachers.


Applicability

Module imported from B.Sc. Data Science.

This module is part of the decentralized Marburg Skills (MarSkills) offering.


Recommended Reading

  • Cormen, Leierson, Rivest, Stein: Algorithmen - Eine Einführung. Oldenbourg.
  • Nebel, Wild: Entwurf und Analyse von Algorithmen (2. Aufl.). Springer Vieweg.
  • Sedgewick, Wayne: Algorithms 4th edition. Pearson.
  • Ottmann, Widmayer: Algorithmen und Datenstrukturen. Spektrum Akad. Verlag.
  • Schöning: Algorithmik. Spektrum Akad. Verlag. 2001.
  • Güting, Dieker: Datenstrukturen und Algorithmen, Vieweg+Teubner



Please note:

This page describes a module according to the latest valid module guide in Winter semester 2025/26. Most rules valid for a module are not covered by the examination regulations and can therefore be updated on a semesterly basis. The following versions are available in the online module guide:

  • Winter 2016/17 (no corresponding element)
  • Summer 2018 (no corresponding element)
  • Winter 2018/19 (no corresponding element)
  • Winter 2019/20 (no corresponding element)
  • Winter 2020/21 (no corresponding element)
  • Summer 2021 (no corresponding element)
  • Winter 2021/22 (no corresponding element)
  • Winter 2022/23 (no corresponding element)
  • Winter 2023/24
  • Winter 2025/26

The module guide contains all modules, independent of the current event offer. Please compare the current course catalogue in Marvin.

The information in this online module guide was created automatically. Legally binding is only the information in the examination regulations (Prüfungsordnung). If you notice any discrepancies or errors, we would be grateful for any advice.