392123 Introduction to Data Mining (V) (WiSe 2026/2027)

The course covers foundational methods of data mining, explorative data analysis, generative modeling, and visualization. The methods will be explained with a focus on applications for educational data mining and learning analytics. The chapters of the course are:

• recap of probability theory
• basic data analysis: outlier detection, data imputation, correlation, and statistical tests, esp. t-tests and signed rank test
• Principal Component Analysis and Factor Analysis
• k-Means, Gaussian Mixture and agglomerative clustering
• item response theory and performance factors analysis
• Markov models and Bayesian Knowledge Tracing
• Variational Autoencoders
• linear and deep time series models
• recommender systems

The core skills taught are:

• understanding the mathematical models and assumptions behind data mining methods and reasoning about them (mathematical reasoning skill)
• implementing data mining models in Python code (implementation skill)
• understanding which method to apply in which cases and how to transform data to make it compatible with the method (application skill)
• critically examining applications of data mining and interpreting models correctly (critical thinking, debugging, and interpretation skill)

The skills are taught in the lecture and complementary teaching videos first, then deepenend in exercises (to be done in small groups), and finally checked in a written exam at the end of the term.

AI tool policy: In the exercises, the use of AI tools is permitted provided that you are transparent about the use and can still explain and defend your work. Undeclared AI use will be sanctioned. In the exam, the use of AI tools is forbidden. Any AI use will be sanctioned.

Requirements for participation, required level

Useful prior knowledge: Neural Networks, Linear Algebra, Probability Theory
Relations to: Information Visualization, Introduction to Machine Learning, Pattern Recognition, Unsupervised Machine Learning, Generative AI

Bibliography

• Baker, Yacef (2009): The State of Educational Data Mining in 2009: A Review and Future Visions. https://jedm.educationaldatamining.org/index.php/JEDM/article/view/8
• Abdi, Williams (2010): Principal component analysis. https://doi.org/10.1002/wics.101
• Jain (2010): Data clustering: 50 years beyond K-means. https://doi.org/10.1016/j.patrec.2009.09.011
• Baker (2001): The basics of item response theory. https://files.eric.ed.gov/fulltext/ED458219.pdf
• Abdelrahman, Wang, Nunes (2022): Knowledge Tracing: A Survey. https://doi.org/10.1145/3569576
• Paaßen, Dywel, Fleckenstein, Pinkwart (2022): Sparse Factor Autoencoders for Item Response Theory. https://files.eric.ed.gov/fulltext/ED624123.pdf