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CS 593 — Neural Networks
(dt. Neuronale Netze)

Level, degree of commitment Specialization module, depends on importing study program
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 examination (individual examination) or written examination
Language,
Grading		 English,
The grading is done with 0 to 15 points according to the examination regulations for the degree program M.Sc. Computer Science.
Subject, Origin Computer Science, M.Sc. Computer Science
Duration,
frequency		 One semester,
each summer semester
Person in charge of the module's outline N.N.

Contents

  • Biological neural networks
  • Supervised learning methods
  • Unsupervised learning methods
  • Theoretical analysis of neural networks
  • Deep Learning
  • Experiment Design and Analysis
  • Possibilities and limitations of the models

Qualification Goals

Translation is missing, sorry. German original:

Die Studierenden

  • können Aspekte der Theorie der neuronalen Netze erklären sowie verschiedenen Architekturen beschreiben und Möglichkeiten und Grenzen künstlicher neuronaler Netze diskutieren,
  • können Konzepte des überwachten Lernens und von Deep Learning erläutern,
  • sind in der Lage, ausgehend von einer konkreten Problemstellung eine datengetriebene Lösung für künstliche Neuronale Netze unter Verwendung von vorgegebenen Programmbibliotheken zu entwerfen,
  • sind in der Lage, wissenschaftliche Arbeitsweisen beim eigenständigen Erkennen, Formulieren und Lösen von Problemen anzuwenden,
  • sind in der Lage, über wissenschaftliche Inhalte frei zu sprechen, sowohl vor einem Publikum als auch in einer Diskussion.

Prerequisites

None. The competences taught in the following modules are recommended: Introduction to Statistics, Machine Learning.


Recommended Reading

  • Ian Goodfellow "Deep Learning. Das umfassende Handbuch: Grundlagen, aktuelle Verfahren und Algorithmen, neue Forschungsansätze" MIT Press
  • Charu C. Aggarwal "Neural Networks and Deep Learning: A Textbook" Springer.



Please note:

This page describes a module according to the latest valid module guide in Winter semester 2025/26. 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.