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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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 Verlag, 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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Data-driven analyses of electronic text books
Boubekki, A., Kröhne, U., Goldhammer, F., Schreiber, W. & Brefeld, U., 2016, Solving large scale learning tasks: Challenges and algorithms : essays dedicated to Katharina Morik on the occasion of her 60th birthday. Michaelis, S., Piatkowski, N. & Stolpe, M. (eds.). Cham: Springer International Publishing, p. 362-376 15 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 9580).Research output: Contributions to collected editions/works › Contributions to collected editions/anthologies › Research
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Feature Extraction and Aggregation for Predicting the Euro 2016
Tavakol, M., Zafartavanaelmi, H. & Brefeld, U., 09.2016, In: CEUR Workshop Proceedings. 1842, 1842, 7 p.Research output: Journal contributions › Conference article in journal › Research › peer-review
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Predicting the future performance of soccer players
Arndt, C. & Brefeld, U., 01.10.2016, In: Statistical Analysis and Data Mining. 9, 5, p. 373-382 10 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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Universal Threshold Calculation for Fingerprinting Decoders using Mixture Models
Schäfer, M., Mair, S., Berchtold, W. & Steinebach, M., 17.06.2015, Proceedings of the 3rd ACM Workshop on Information Hiding and Multimedia Security. Association for Computing Machinery, Inc, p. 109-114 6 p. (IH and MMSec 2015 - Proceedings of the 2015 ACM Workshop on Information Hiding and Multimedia Security).Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review
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Using Wikipedia for Cross-Language Named Entity Recognition
Fernandes, E. R., Brefeld, U., Blanco, R. & Atserias, J., 2016, Big Data Analytics in the Social and Ubiquitous Context: 5th International Workshop on Modeling Social Media, MSM 2014, 5th International Workshop on Mining Ubiquitous and Social Environments, MUSE 2014, and First International Workshop on Machine Learning for Urban Sensor Data, SenseML 2014, Revised Selected Papers. Atzmüller, M., Chin, A., Janssen, F., Schweizer, I. & Trattner, C. (eds.). Springer International Publishing, p. 1-25 25 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 9546).Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review
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Spatio-Temporal Convolution Kernels
Knauf, K., Memmert, D. & Brefeld, U., 01.02.2016, In: Machine Learning. 102, 2, p. 247-273 27 p.Research output: Journal contributions › Journal articles › Research › peer-review
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Nmap: A novel neighborhood preservation space-filling algorithm
Duarte, F. S. L. G., Sikansi, F., Fatore, F. M., Fadel, S. G. & Paulovich, F. V., 31.12.2014, In: IEEE Transactions on Visualization and Computer Graphics. 20, 12, p. 2063-2071 9 p., 6876012.Research output: Journal contributions › Conference article in journal › Research › peer-review