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CS 503 — Applications of Machine Learning
(dt. Anwendungen von Maschinellem Lernen)

Level, degree of commitment Practical 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.
Examination type: Oral examination (individual examination) or written examination
Language,
Grading		 German,
The grading is done with 0 to 15 points according to the examination regulations for the degree program B.Sc. Computer Science.
Subject, Origin Computer Science, Export only modules
Duration,
frequency		 One semester,
unregelmäßig
Person in charge of the module's outline Dr. Markus Mühling

Contents

  • Clarification of basic terms (AI, machine learning, deep learning, ...).
  • Fundamentals of supervised, unsupervised and reinforcement learning with concrete use cases.
  • Introduction to the process of building AI models: Data acquisition, data preparation, data preprocessing, model building/training, analysis/evaluation, model optimization, deployment.
  • Basics of neural networks (network architectures, learning strategies, convolutional neural networks)
  • Deep Learning Framework: Tensorflow
  • AI application examples: Price prediction, NLP, image recognition, image segmentation
  • AI and ethics

Qualification Goals

Students will

  • know basic functionalities of machine learning,
  • know possibilities and limits of the use of machine learning,
  • are able to use machine learning techniques on the basis of exemplary applications,
  • are able to apply scientific working methods when independently identifying, formulating and solving problems.

Prerequisites

None.


Recommended Reading

  • Will be announced in the module announcement.



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:

  • 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

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.