Main content

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 Prof. Dr. Dominik Heider

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

The students

  • have an insight into the theory of neural networks as well as an overview of the different architectures, possibilities and limitations of artificial neural networks,
  • are familiar with supervised learning and deep learning
  • are able to design a data-driven solution for artificial neural networks based on a concrete problem using given program libraries,
  • are able to apply scientific working methods when independently identifying, formulating and solving problems,
  • are able to speak freely about scientific content, both in front of an audience and in a discussion.

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 2023/24. 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.