Main content

This entry is from Winter semester 2020/21 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 M.Sc. Mathematics, this module can be attended in the study area Minor subject Computer Science.


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 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:

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.