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This entry is from Winter semester 2016/17 and might be obsolete. No current equivalent could be found.

CS 692 — Databionics
(dt. Datenbionik)

Level, degree of commitment Specialization module, depends on importing study program
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 M.Sc. Computer Science.
Subject, Origin Computer Science, M.Sc. Computer Science
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

Translation is missing. Here is the German original:

Keine. Empfohlen werden die Kompetenzen, die in den Basismodulen der Informatik vermittelt werden


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 2016/17. 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.