(dt. Neuronale Netze)
|Level, degree of commitment in original study programme||Advanced module, compulsory elective module|
|Forms of teaching and learning,
|Lecture (2 SWS), recitation class (2 SWS), |
180 hours (60 h attendance, 120 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|
- Biological neural networks
- Supervised learning procedures
- Unsupervised learning procedures
- Theoretical Analysis of Neural Networks
- Self-organization and emergence
- Experiment design and analysis
- Capabilities and limits of the models
The students shall
- have an insight into the theory of neural networks and an overview of the different architectures, chances and limitations of artificial neural networks,
- in addition to the common supervised learning networks, acquire knowledge of unsupervised learning neural networks and the paradigm of self-organization and emergence,
- be able to design a data-driven solution for artificial neural networks based on a concrete problem using predefined program libraries,
- 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, Algorithms and Data Structures.
- N. Cristianini and J. Shawe-Taylo: An Introduction to Support Vector Machines and Other Kernel-based Learning Methods, Cambridge University Press, 2000. Raul Rojas: Theorie der neuronalen Netze, Springer.
- Ritter, H: Neuronale Nezte, Addison-Wesley.
This page describes a module according to the latest valid module guide in Wintersemester 2020/21. 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.