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CS 692 — Databionics
(dt. Datenbionik)

Level, degree of commitment Specialization 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 or written examination
Language,
Grading		 English,
The grading is done with 0 to 15 points according to the examination regulations for the degree program M.Sc. Data Science.
Duration,
frequency		 One semester,
irregular
Person in charge of the module's outline Prof. Dr. Michael Thrun

Contents

Data bionics means the transfer of algorithms for data processing from nature. Examples are artificial neural networks and genetic algorithms.

  • Introduction to the well-known theories of nature-analogous information processing
  • Connectionist Models
  • Evolutionary and Genetic Algorithms
  • Swarm Intelligence & Artificial Life
  • Ant Colony Optimization & Particle Swarm Optimization

Qualification Goals

Translation is missing, sorry. German original:

Die Studierenden

  • können gebräuchliche Datenbionische Methoden darstellen und anwenden,
  • können die Möglichkeiten und Grenzen naturanaloger Informationsverarbeitung diskutieren,
  • sind in der Lage, ausgehend von einer konkreten Problemstellung, eine Lösung mittels datenbionischer Methoden zu entwerfen,
  • sind in der Lage, wissenschaftliche Arbeitsweisen beim eigenständigen Erkennen, Formulieren und Lösen von Problemen anzuwenden,
  • sind in der Lage, über wissenschaftliche Inhalte frei zu sprechen, sowohl vor einem Publikum als auch in einer Diskussion.

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, Declarative Programming, Object-oriented Programming, System Software and Computer Communication, Technical Computer Science.


Applicability

Module imported from M.Sc. Data Science.

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

  • B.Sc. Data Science
  • B.Sc. Computer Science
  • M.Sc. Data Science
  • M.Sc. Computer Science

When studying M.Sc. Computer Science, this module can be attended in the study area Compulsory Elective Modules in Computer Science.

The module is assigned to Practical Computer Science. Further information on eligibility can be found in the description of the study area.


Recommended Reading

  • R. Rojas, Theorie der neuronalen Netze, Springer 1996
  • T. Kohonen, Self-Organizing Maps, Springer, 2003
  • E. Bonabeu, M. Dorigo, G. Theraulaz, Swarm Intelligence, 1999
  • D. Goldberg, Genetic Algorithms in Search, Optimization and Machine Learning, Kluwer Academic Publishers, 2001
  • Ashlock, D.Evolutionary Computation for Modeling and Optimization, Springer,2006



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:

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.