Requires basic knowledge of data bases, statistics and experience with a modern programming Language

- Introduction to data mining: overview, CRISP, data pre-processing, concepts of supervised and unsupervised learning, visual analytics
- Association rules
- Linear regression: simple linear regression, introduction to multiple linear regression
- Classification: logistic regression, decision trees, SVM
- Ensemble methods: bagging, random forests, boosting
- Clustering: K-means, K-medoids, Hierarchical clustering
- Evaluation and validation: cross-validation, assessing the statistical significance of data mining results
- Ethics and privacy
- Selection of advanced topics such as neural networks, outlier detection, relation to big data analysis
- In the lab, students apply data mining methods and algorithms to problem sets and develop data mining applications,
using tools such as R and RapidMiner

written exam, 60 Min. and report (Data Mining, Lab Data Mining)

written exam, 60 min. (K60, Data Mining) and report (BE, Lab Data Mining)

Prof. Dr. Stephan Trahasch

Strategic Crisis Management

Strategic Risk Management