ECTS: 5

[2.4 -AUTO System Automation (SL)]

The students learn various aspects of scientific communication: reading, writing, presenting, as well as ethical aspects of IT, licensing issues, and career management.

ECTS: 3

[M17 Masterseminar: Scientific Communication]

The students learn various aspects of scientific communication: reading, writing, presenting, as well as ethical aspects of IT, licensing issues, and career management.

ECTS: 5

[M8.1 Advanced Statistical Learning]

• Introduction to statistical learning methods for use in finance and insurance
• Unsupervised learning: cluster analysis for asset allocation and portfolio optimization, principal component analysis,
• Supervised learning - classification and regression: Logistic regression applied to default probabilities, Support Vector Machines, Decision trees (CART) for credit risk analysis
• Introduction to recurrent neural networks for modeling stock price processes
• Ensemble techniques: Bootstrap, bagging, random forest, boosting, cross-validation. XAI - Explainable AI: Shapley values and LIME (Local Interpretable Model-agnostic)
• Introduction to reinforcement learning: optimal stock execution, option pricing
• Methods and algorithms in R, R packages and overview of methods in Python

ECTS: 5

[M1.1 Business Administration Applications I (SL/PCÜ)]

Students have the ability to analyse and model complex, integrated business processes. They are able to realise integrated industry- and company-specific business processes with complex business application systems. To this end, they acquire business management and methodological knowledge as well as practical skills for selecting and customising standard software. Students also acquire the ability to evaluate current trends as well as legal, technological and economic framework conditions on the basis of scientific literature and their own practical experience. They are able to present their solutions and assessments both in writing and orally on a scientific basis.

ECTS: 5

[1.4 - SE Systems Engineering (SL)]

Students gain knowledge and abilities in basic methods and tasks of system engineering. They are able to understand and analyse the taught data sets and methods and are able to sort them and put them inot perspective.

Students learn about:

• methods and techniques of system analysis
• system definition and structure
• proces of problem solving, management and optimisation.

Students are able to analyse and describe systems and design target models.

ECTS: 6

[M13 Forschungsorientiertes Wissenschaftliches Arbeiten]

Content: Further information to follow.

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