Data Exploration and System Management Using Artificial Intelligence / Machine Learning
Datenexploration und Systemmanagement mit Künstlicher Intelligenz / Maschinellem Lernen
6 CPirregularlyEN
Prerequisites
Recommended, as written
• Knowledge of mathematics, especially statistics
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.
Learning outcomes
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.
Assessment
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.
Literature (2)
- 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.
Remarks
• 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“
Source: BTU's module description · retrieved 14 Oct 2026Original