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German original

Databionics
(dt. Datenbionik)

Level, degree of commitment in original study programme 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: 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
German,
The grading is done with 0 to 15 points according to the examination regulations for study course M.Sc. Computer Sciences.
Original study programme M.Sc. Informatik / Vertiefungsbereich Informatik
Duration,
frequency
One semester,
each summer semester
Person in charge of the module's outline Prof. Dr. Alfred Ultsch

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

The students shall

  • learn common databionic methods,
  • learn the possibilities and limits of nature-analogous information processing,
  • be able to design a solution using data-bionic methods based on a concrete problem,
  • practice scientific working methods (recognizing, formulating, solving problems, training the ability to abstract),
  • practice oral communication skills in the exercises by practicing free speech in front of an audience.

Prerequisites

None. The competences taught in the following modules are recommended: Object-oriented Programming, either Algorithms and Data Structures or Practical Informatics II: Data Structures and Algorithms for Pre-Service-Teachers, System Software and Computer Communication.


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 Wintersemester 2022/23. 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.