Statistics and Concepts of AI
- Faculty
Faculty of Business Management and Social Sciences
- Version
Version 1 of 20.03.2026.
- Module identifier
22M1207
- Module level
Master
- Language of instruction
German
- ECTS credit points and grading
5.0
- Module frequency
only summer term
- Duration
1 semester
- Brief description
The master's module "Statistics and Concepts of AI" provides students in business sciences with comprehensive knowledge of statistical methods and their application in the field of artificial intelligence, grounded in the work with IT-tools. Participants learn how to analyze and interpret data sets in order to make informed business decisions. The module covers both traditional statistical techniques and modern concepts of artificial intelligence, such as machine learning and data-driven modeling. Through a combination of theoretical knowledge and practical exercises, students are prepared to effectively apply AI technologies in business contexts.
- Teaching and learning outcomes
1. Advanced Statistical Methods: Statistical Inference, Hypothesis Testing and Confidence Intervals, Multiple Regression, Logistic Regression
2. Fundamentals of Machine Learning (ML): Supervised vs. Unsupervised Learning, Model Evaluation, Basic Neural Networks
3. Current AI Applications and Architectures: Data Collection and Generation, Data Preparation, Specific Data Structure Characteristics for Each ML Method, Architectural Consequences of Data Structures.Especially 1. and 2. are supported by the active work with IT-Tools.
- Overall workload
The total workload for the module is 150 hours (see also "ECTS credit points and grading").
- Teaching and learning methods
Lecturer based learning Workload hours Type of teaching Media implementation Concretization 45 Seminar Presence or online - Lecturer independent learning Workload hours Type of teaching Media implementation Concretization 20 Work in small groups - 30 Preparation/follow-up for course work - 15 Study of literature - 30 Exam preparation - 10 Reception of other media or sources -
- Graded examination
- Written examination or
- Portfolio exam or
- Portfolio exam
- Knowledge Broadening
Advanced Statistical Knowledge: Students expand their understanding of advanced statistical methods beyond the basics. They learn how to conduct complex data analyses, test hypotheses, and apply models such as multiple and logistic regression.
Fundamentals of Machine Learning: Students receive a comprehensive introduction to the principles of machine learning. They learn to distinguish between supervised and unsupervised learning methods and critically evaluate ML models. They are also introduced to the fundamentals of neural networks and their current architectures.
Learners also widen their knowledge about the application of IT-Tools, which model (with) statistical methods and Machine Learning.
- Knowledge deepening
The module provides in-depth insights into the use of statistics and machine learning for assessing and managing risks. Students learn how statistical models and AI can be used to analyze risk profiles and support decision-making processes.
Additionally, the module addresses the integration of data sources and data analysis: by applying IT-Tools, students deepen their knowledge of how various data sources can be effectively integrated to create comprehensive and accurate financial analyses. They learn to analyze and utilize data from internal and external sources to support strategic decision-making.
- Knowledge Understanding
Graduates assess the validity and accuracy of machine learning models by incorporating advanced quantitative methods and data analytical considerations, and they are capable of solving practical problems in the finance and controlling sectors using these assessments.
- Literature
Field, Andy, Zoe Field, and Jeremy Miles. "Discovering statistics using R." (2012): 1-992.
Murphy, Robin, and David D. Woods. "Beyond Asimov: The three laws of responsible robotics." IEEE intelligent systems 24.4 (2009): 14-20.
Gonçalves, Bernardo. "The Turing test is a thought experiment." Minds and Machines 33.1 (2023): 1-31.
Hyndman, R. J. Forecasting: principles and practice. OTexts, 2018.
Sarker, Iqbal H. "Machine learning: Algorithms, real-world applications and research directions." SN computer science 2.3 (2021): 160.
Raschka, Sebastian, and Vahid Mirjalili. Python machine learning: Machine learning and deep learning with Python, scikit-learn, and TensorFlow 2. Packt publishing ltd, 2019.
Boehmke, Brad, and Brandon M. Greenwell. Hands-on machine learning with R. Chapman and Hall/CRC, 2019.
Anmerkung: Die Literaturliste und damit auch das Modul nehmen ausdrücklich Abstand vom modellierenden und datenauswertenden Einsatz der Werkzeuge Excel und SPSS. R und Python und auch die entsprechenden Software-Pakete sind auf dieser Literaturliste als Beispiele für in der Modulbeschreibung genannte "IT-Tools" zu sehen. Es ist nicht ausgeschlossen, dass ein technisches Update der Literaturliste unter Inklusion weiterer Programmiersprachen nötig sein wird, da das Wissensgebiet hochdynamisch ist.
- Applicability in study programs
- Controlling and Finance
- Controlling and Finance M.Sc. (01.09.2026)
- Person responsible for the module
- Faatz, Andreas
- Teachers
- Markovic-Bredthauer, Danijela
- Dallmöller, Klaus
- Bensberg, Frank
- Schmidt, Andreas
- Uliczka, Jan
- Faatz, Andreas