Knowledge Representation and Machine Learning

In summer term, we offer the courses:

• User Studies in interactive intelligent Systems (Seminar + Exercises) for bachelor students in the module 39-Inf-WP-MTI (Human-Machine Interaction) and masters students in the modules 39-M-Inf-INT-app Applied Interaction Technology or 39-M-Inf-INT-app-foc Applied Interaction Technology (focus).
• Human-Centric Machine Learning (Seminar + Exercises) for bachelor students as part of the module 39-Inf-WP-IG Computer Science and Society.

In winter term, we offer the courses:

• Introduction to Data Mining (lecture + exercises) for bachelor students in the module 39-Inf-DM Grundlagen Datamining, 39-Inf-WP-DS Data Science (Basis) and 39-Inf-WP-DS-x Data Science (Schwerpunkt), as well as for master students in the module 39-M-Inf-AI-bas Basics of Artificial Intelligence.
• Applied Optimization (lecture + exercises) as mandatory course for data science masters students; and as voluntary course for bachelor students in the module 39-Inf-DM Grundlagen Datamining, 39-Inf-WP-DS Data Science (Basis) and 39-Inf-WP-DS-x Data Science (Schwerpunkt), as well as for master students in the module 39-M-Inf-AI-bas Basics of Artificial Intelligence.

We are also active in further activities to improve teaching with our research.

Theses

From WS2025/2026 we offer theses again. In particular, the following topics are open:

- Developing a language model-based annotation plugin for psychology software and evaluating it in a user study (together with the psychology department)
- Developing a retrieval augmented generation module for our intelligent tutoring system to automatically generate hints that refer to teaching material and evaluating it
- Using Ensemble methods to investigate the limitations of LLM-generated text detection
- Explaining (the limits of) LLM-generated text detection: why was this text detected as LLM-generated?
- Further topics will be added

If you are interested, please contact:

Prof. Benjamin Paassen
+49 521 106-87838
bpaassen@techfak.uni-bielefeld.de

 

The following theses have already been completed in our group

• Development and Evaluation of a Categorization Interface for Social Psychology Experiments (MA thesis by Julian Goergen; supervised by Prof. Benjamin Paaßen and Prof. Thekla Morgenroth)
• Adaptive Lernumgebungen basierend auf Cognitive Load - Evaluation anhand eines Prototyps zum Thema Wald (MA thesis by Nikolas Weber; supervised by Prof. Benjamin Paaßen and Dr. Daniela Sellmann-Risse)
• Prototype-Based Convolutional Neural Networks for EMG signal classification (BA thesis by Dennis Schielke; supervised by Prof. Benjamin Paaßen and Rui Liu)
• Design and Evaluation of a Word-Clustering-Tool for Social Psychology (BA thesis by Luis Klocke; supervised by Prof. Benjamin Paaßen and Dr. Adia Khalid)
• Partial Credit Item Response Autoencoders (BA thesis by Lukas Wüppelmann; supervised by Prof. Benjamin Paaßen and Jesper Dannath)
• Prototype Decision Trees via Learning Vector Quantization (BA thesis by Arno Gaußelmann; supervised by Prof. Benjamin Paaßen and Valerie Vaquet)
• Automatic Unit-Tests Generation via Large Language Models for Feedback in Programming Education (BA thesis by Lukas Morasch; supervised by Prof. Benjamin Paaßen and Jesper Dannath)
• Comparing Large Language Models for Automatic Unit Test Generation in a Python Course (BA thesis by Richard Pamies; supervised by Prof. Benjamin Paaßen and Jesper Dannath)
• Text-based difficulty estimation for multiple choice questions (BA thesis by Niklas Rotter; supervised by Prof. Benjamin Paaßen and Alonso Palomino Garibay)
• Comparing Two-State Hidden Markov Models with Covariates to a Hierarchical approach using Clustering and Logistic Regression (BA thesis by Özay Öztürk; supervised by Prof. Benjamin Paaßen and Prof. Roland Langrock)
• Few-Shot Multilabel Text Classification of Student Counseling Questions (BA thesis by Jasper Matzat; supervised by Prof. Benjamin Paaßen and Jesper Dannath)
• Personalized Chess Training Plans via Multidimensional Item Response Theory (BA thesis by Luca Sander; supervised by Prof. Benjamin Paaßen and Alina Deriyeva)
• Generative KI im agilen Software Engineering - Eine Fallstudie in der universitären Ausbildung (BA thesis by Luca Strignano; supervised by Prof. Benjamin Paaßen and Dr. Sebastian Wrede)
• Adapt-Leaf-Forest: ein innovativer Ansatz für die Klassifikation mit Random Forest (BA thesis by Maurice Heidemann; supervised by Prof. Benjamin Paaßen and Prof. Kevin Tierney)
• Learning to Style Check (MA thesis by Tim Marvin Heptner; supervised by Prof. Benjamin Paaßen and Dr. Alexander Schulz)
• Optimal Team Composition and Skill Estimation in Pair Sports (MA thesis by Sören Rüttgers; supervised by Prof. Benjamin Paaßen and Dr. Ulrike Kuhl)