Main content
This entry is from Summer semester 2018 and might be obsolete. No current equivalent could be found.
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): 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. |
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.
Applicability
Module imported from M.Sc. Computer Science.
It can be attended at FB12 in study program(s)
- B.Sc. Computer Science
- 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. Business Informatics, this module can be attended in the study area Specialization in Computer Science.
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 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:
- Winter 2016/17
- Summer 2018
- Winter 2018/19
- Winter 2019/20
- Winter 2020/21
- Summer 2021
- Winter 2021/22
- Winter 2022/23
- Winter 2023/24 (no corresponding element)
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.