|Level, degree of commitment in original study programme||Advanced module, compulsory elective module|
|Forms of teaching and learning,
|Lecture (4 SWS), recitation class (2 SWS), |
270 hours (90 h attendance, 180 h private study)
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
|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|
|One semester, |
each summer semester
|Person in charge of the module's outline||Prof. Dr. Alfred Ultsch|
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
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.
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.
- 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
This page describes a module according to the latest valid module guide in Wintersemester 2019/20. 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:
- WiSe 2016/17 (no corresponding element)
- SoSe 2018 (no corresponding element)
- WiSe 2018/19
- WiSe 2019/20
- WiSe 2020/21
- SoSe 2021
- WiSe 2021/22
- WiSe 2022/23
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.