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CS 621 — Deep Learning
(dt. Deep Learning)

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 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. Data Science.
Duration,
frequency		 One semester,
irregular
Person in charge of the module's outline N.N.

Contents

Translation is missing, sorry. German original:

N.N.


Qualification Goals

Translation is missing, sorry. German original:

Die Studierenden

  • können die Grundlagen von tiefen neuronalen Netzen und Deep Learning erläutern,
  • können gängige Techniken zur Optimierung und Regularisierung in Deep Learning beschreiben,
  • können gängige Architekturen von tiefen neuronalen Netzwerken erklären,
  • können Deep Learning frameworks anwenden, um Lösungen in einem Anwendungsgebiet zu implementieren,
  • können neue Entwicklungen im Bereich Deep Learning aufzeigen,
  • sind in der Lage, wissenschaftliche Arbeitsweisen beim eigenständigen Erkennen, Formulieren und Lösen von Problemen anzuwenden.

Prerequisites

None. The competences taught in the following modules are recommended: Machine Learning, either Introduction to Statistics or Elementary Probability and Statistics or Elementary Stochastics.


Applicability

The module can be attended at FB12 in study program(s)

  • B.Sc. Data Science
  • B.Sc. Computer Science
  • M.Sc. Data Science
  • M.Sc. Computer Science
  • M.Sc. Mathematics
  • M.Sc. Business Informatics

When studying M.Sc. Data Science, this module can be attended in the study area Free Compulsory Elective Modules.

The module can also be used in other study programs (export module).


Recommended Reading

(not specified)



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:

  • Winter 2016/17 (no corresponding element)
  • Summer 2018 (no corresponding element)
  • Winter 2018/19 (no corresponding element)
  • Winter 2019/20 (no corresponding element)
  • Winter 2020/21 (no corresponding element)
  • Summer 2021 (no corresponding element)
  • Winter 2021/22 (no corresponding element)
  • Winter 2022/23 (no corresponding element)
  • Winter 2023/24 (no corresponding element)
  • Winter 2025/26

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.