Professorship for Information Systems, in particular Machine Learning
Organisational unit: Professoship
Main research areas
Research
We are interested in statistical machine learning with a focus on spatiotemporal problems, such as user navigation on the web, adaptive testing and adaptive learning environments, and the coordination of football players on the pitch. While we mainly focus on basic research, we also collaborate with selected partners in academia, sports and industry in different projects.
Teaching
Our teaching focuses on introductory/advanced machine learning and data mining as well as basic statistics. We regularly offer courses in the Management & Data Science Master and the Information Systems Bachelor programs. Exemplary courses comprise Deep Learning (Data Science), Statistics (Information Systems), and Machine Learning & Data Mining (Engineering).
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Contextual movement models based on normalizing flows
Fadel, S., Mair, S., da Silva Torres, R. & Brefeld, U., 03.2023, In: AStA Advances in Statistical Analysis. 107, 1-2, p. 51-72 22 p.Research output: Journal contributions › Journal articles › Research › peer-review
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Constrained Independence for Detecting Interesting Patterns
Delacroix, T., Boubekki, A., Lenca, P. & Lallich, S., 02.12.2015, 2015 IEEE International Conference on Data Science and Advanced Analytics (DSAA). Pasi, G., Kwok, J., Zaiane, O., Gallinari, P., Gaussier, E. & Cao, L. (eds.). IEEE - Institute of Electrical and Electronics Engineers Inc., 10 p. 7344897. (Proceedings of the 2015 IEEE International Conference on Data Science and Advanced Analytics, DSAA 2015).Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review
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A Unified Contextual Bandit Framework for Long- and Short-Term Recommendations
Tavakol, M. & Brefeld, U., 30.12.2017, Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2017 Skopje, Macedonia, September 18 – 22, 2017; Proceedings, Part II. Ceci, M., Hollmen, J., Todorovski, L., Vens, C. & Dzeroski, S. (eds.). Springer, Vol. 2. p. 269-284 16 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 10535 LNAI).Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review
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Assessing User Behavior by Mouse Movements
Matthiesen, J. J. & Brefeld, U., 2020, HCI International 2020 - Posters - : 22nd International Conference, HCII 2020, Proceedings. Stephanidis, C. & Antona, M. (eds.). Cham: Springer, p. 68-75 8 p. (Communications in Computer and Information Science; vol. 1224 CCIS).Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review
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Analysing Positional Data
Brefeld, U., Mair, S. & Lasek, J., 01.10.2020, Science Meets Sports: When Statistics Are More Than Numbers. Ley, C. & Dominicy, Y. (eds.). Newcastle upon Tyne: Cambridge Scholars Publishing, p. 81-94 13 p. (Physical Sciences).Research output: Contributions to collected editions/works › Chapter › peer-review
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Adaptive Item Selection Under Matroid Constraints
Bengs, D., Brefeld, U. & Kröhne, U., 07.08.2018, In: Journal of Computerized Adaptive Testing. 6, 2, p. 15-36 22 p.Research output: Journal contributions › Journal articles › Research › peer-review
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Action rate models for predicting actions in soccer
Dick, U. & Brefeld, U., 02.03.2022, In: AStA Advances in Statistical Analysis. 107, 1-2, p. 29-49 21 p.Research output: Journal contributions › Journal articles › Research › peer-review