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This entry is from Winter semester 2022/23 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): 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 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
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.
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 Mathematics, this module can be attended in the study area Specialization Modules.
The module is assigned to 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 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:
- Winter 2016/17 (no corresponding element)
- 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.