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