13906 - Data Exploration and System Management Using Artificial Intelligence / Machine Learning Modulübersicht

Module Number: 13906
Module Title:Data Exploration and System Management Using Artificial Intelligence / Machine Learning
  Datenexploration und Systemmanagement mit Künstlicher Intelligenz / Maschinellem Lernen
Department: Faculty 1 - Mathematics, Computer Science, Physics, Electrical Engineering and Information Technology
Responsible Staff Member:
  • Prof. Dr.-Ing. Dr. rer. nat. habil. Schenk, Harald
Language of Teaching / Examination:English
Duration:1 semester
Frequency of Offer: On special announcement
Credits: 6
Learning Outcome:After completion of the module, students will have an overview of the basic operations available for processing datasets measured in systems of any nature (e.g., physical, technical, biological, financial, etc.) and will be able to relate these operations to AI/ML-based methods and tools dedicated to a specific task. The students understand real world problems and can explain them using computer simulations. They know how to apply the acquired knowledge to an individual project, including increased skills in preparing project documentation and public presentation.
Contents:The subject of the module are the classes of real-world problems that can be solved by data exploration using AI/ML methods. This includes, for example, anomaly/outlier detection, data decomposition and feature selection, data fusion, prediction, decision support. A mapping between problems and available AI/ML methods will be presented.
The project consists in solving a self-defined problem using a selected AI/ML technique and computer simulations. The software procedure together with a project report will be created by student.
Recommended Prerequisites:
  • Knowledge of mathematics, especially statistics
Mandatory Prerequisites:None
Forms of Teaching and Proportion:
  • Lecture / 2 Hours per Week per Semester
  • Study project / 2 Hours per Week per Semester
  • Self organised studies / 120 Hours
Teaching Materials and Literature:
  • Hastie T., Tibshirani R., Friedman J.: The elements of statistical learning. Data mining, inference, and prediction. Springer, 2nd edition, New York 2009.
  • Kulkarni S., Harman G.: Elementary introduction to statistical learning theory. Wiley & Sons Inc., New Jersey 2011.
Module Examination:Prerequisite + Final Module Examination (MAP)
Assessment Mode for Module Examination:Prerequisite:
  • Successful completion of the project task, 30 h
Final module examination:
  • Written examination, 90 min. OR
  • Oral examination, 30-45 min. (with small number of participants)
In the first lecture it will be announced, wheter the examination will be organised in written or oral form.
Evaluation of Module Examination:Performance Verification – graded
Limited Number of Participants:20
Part of the Study Programme:
  • Master (research-oriented) / Artificial Intelligence / PO 2022
  • Abschluss im Ausland / Informatik / keine PO
  • Master (research-oriented) / Physics / PO 2021
  • Study programme Artificial Intelligence M.Sc.: Compulsory elective module in complex  „Learning and Reasoning“
  • Study programme Physics M.Sc.: Compulsory elective module in complex „Minor Subject“
Module Components:
  • Lecture: Data exploration and system management using AI/ML
  • Accompanying project
  • Related examination
Components to be offered in the Current Semester:
  • no assignment