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This entry is from Winter semester 2019/20 and might be obsolete. You can find a current equivalent here.
CS 593 — Neural Networks
(dt. Neuronale Netze)
Level, degree of commitment | Specialization module, compulsory elective module |
Forms of teaching and learning, workload |
Lecture (2 SWS), recitation class (2 SWS), 180 hours (60 h attendance, 120 h private study) |
Credit points, formal requirements |
6 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
- 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
Qualification Goals
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.
Prerequisites
None. The competences taught in the following modules are recommended: Object-oriented Programming, Algorithms and Data Structures.
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 Mathematics
- LAaG Computer Science
When studying B.Sc. Computer Science, this module can be attended in the study area Compulsory Elective Modules in Computer Science.
Die Wahlmöglichkeit des Moduls ist dadurch beschränkt, dass es der Praktischen Computer Science zugeordnet ist.
Recommended Reading
- 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.
Please note:
This page describes a module according to the latest valid module guide in Winter semester 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:
- 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
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.