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This entry is from Summer semester 2018 and might be obsolete. You can find a current equivalent here.

CS 566 — 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): Oral or written examination
Examination type: Successful completion of at least 50 percent of the points from the weekly exercises as well as at least 2 presentations of the tasks.
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,
irregular
Person in charge of the module's outline Prof. Dr. Bernhard Seeger

Contents

Algorithmic techniques

  • Greedy algorithms
  • Dynamic programming
  • Divide-and-conquer
  • space and time complexity (worst-case, amortised, output-sensitive, resolution of running time recurrences)
  • Correctness proofs
  • Algorithms for set problems, graph problems, text problems and geometric problems
  • Algorithms for external memory models
  • Algorithms for data streams
  • Approximation algorithms

Qualification Goals

  • Algorithm design skills,
  • Knowledge of the most important design and analysis paradigms,
  • Use of efficient data structures for the design of algorithms,
  • Insights into the analysis of algorithms with respect to correctness and efficiency
  • Practice of scientific methods (recognition, formulation, and solution of problems, training of abstract thinking),
  • Training of oral communication skills in the exercises by practicing free speech in front of an audience and during discussion.

Prerequisites

Translation is missing. Here is the German original:

Keine. Empfohlen werden die Kompetenzen, die in dem Modul Algorithmen und Datenstrukturen vermittelt werden.


Applicability

Module imported from B.Sc. Data Science.

It can be attended at FB12 in study program(s)

  • B.Sc. Data Science
  • B.Sc. Computer Science
  • B.Sc. Business Informatics
  • M.Sc. Data Science
  • M.Sc. Computer Science
  • M.Sc. Mathematics
  • M.Sc. Business Informatics
  • M.Sc. Business Mathematics
  • LAaG Computer Science

When studying M.Sc. Mathematics, this module can be attended in the study area Minor subject Computer Science.


Recommended Reading

  • Cormen, Leierson, Rivest, Stein: Algorithmen - Eine Einführung. Oldenbourg.
  • 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 Summer semester 2018. 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:

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.