Prof. Dr. Olaf Mersmann
Dr. rer. nat.
Fakultät für Informatik und Ingenieurwissenschaften
Institut für Data Science, Engineering, and Analytics (IDE+A)
Technische Hochschule Köln
Steinmüllerallee 6
51643 Gummersbach
Raum 1.503
Postanschrift
+49 2261-8196-6564
olaf.mersmann@th-koeln.de
Sprechstunden
Montag, 16.00 bis 17.00 Uhr
Campus Gummersbach, Raum LC6 1.503
In den Semesterferien nur nach vorheriger Terminvereinbarung
Lehrgebiete
- Angewandte Mathematik
- Statistik
- Data Mining (Master Informatik, Master Medieninformatik)
- Data Mining (Bachelor Informatik, Master Medieninformatik)
- Data Science & Ethics (Master Data Science)
Forschungsgebiete
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Explorative Landschaftsanalyse
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Benchmarking
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Maschinelles Lernen
Publikationen
- Dietrich, Konstantin; Mersmann, Olaf (2022): Increasing the Diversity of Benchmark Function Sets Through Affine Recombination. In: Parallel Problem Solving from Nature – PPSN XVII : 17th International Conference, PPSN 2022, Dortmund, Germany, September 10–14, 2022, Proceedings, Part I. 17th International Conference, PPSN 2022, Dortmund, Germany, 10.09.-14.09.2022., S. 590 - 602. (peer-reviewed)
- Bartz, Eva; Bartz-Beielstein, Thomas; Zaefferer, Martin; Mersmann, Olaf (Hrsg.) (2022): Hyperparameter Tuning for Machine and Deep Learning with R : A Practical Guide. Singapore: Springer Nature. (Open Access)
- Bartz-Beielstein, Thomas; Mersmann, Olaf; Chandrasekaran, Sowmya (2022): Ranking and Result Aggregation. In: Bartz, Eva; Bartz-Beielstein, Thomas; Zaefferer, Martin; Mersmann, Olaf (Hrsg.): Hyperparameter Tuning for Machine and Deep Learning with R : A Practical Guide. Singapore: Springer Nature, S. 121 - 161. (peer-reviewed/Open Access)
- Bartz-Beielstein, Thomas; Zaefferer, Martin (2022): Hyperparameter Tuning Approaches. In: Bartz, Eva; Bartz-Beielstein, Thomas; Zaefferer, Martin; Mersmann, Olaf (Hrsg.): Hyperparameter Tuning for Machine and Deep Learning with R : A Practical Guide. Singapore: Springer Nature, S. 71 - 119. (peer-reviewed/Open Access)
- Bartz-Beielstein, Thomas; Zaefferer, Martin (2022): Models. In: Bartz, Eva; Bartz-Beielstein, Thomas; Zaefferer, Martin; Mersmann, Olaf (Hrsg.): Hyperparameter Tuning for Machine and Deep Learning with R : A Practical Guide. Singapore: Springer Nature, S. 27 - 69. (peer-reviewed/Open Access)
- Bartz-Beielstein, Thomas; Zaefferer, Martin; Mersmann, Olaf (2022): Tuning : Methodology. In: Bartz, Eva; Bartz-Beielstein, Thomas; Zaefferer, Martin; Mersmann, Olaf (Hrsg.): Hyperparameter Tuning for Machine and Deep Learning with R : A Practical Guide. Singapore: Springer Nature, S. 7 - 26. (peer-reviewed/Open Access)
- Bartz-Beielstein, Thomas; Chandrasekaran, Sowmya; Rehbach, Frederik (2022): Case Study III: Tuning of Deep Neural Networks. In: Bartz, Eva; Bartz-Beielstein, Thomas; Zaefferer, Martin; Mersmann, Olaf (Hrsg.): Hyperparameter Tuning for Machine and Deep Learning with R : A Practical Guide. Singapore: Springer Nature, S. 235 - 269. (peer-reviewed/Open Access)
- Bartz-Beielstein, Thomas; Chandrasekaran, Sowmya; Rehbach, Frederik (2022): Case Study II : Tuning of Gradient Boosting (xgboost). In: Bartz, Eva; Bartz-Beielstein, Thomas; Zaefferer, Martin; Mersmann, Olaf (Hrsg.): Hyperparameter Tuning for Machine and Deep Learning with R : A Practical Guide. Singapore: Springer Nature, S. 221 - 234. (peer-reviewed/Open Access)
- Bartz-Beielstein, Thomas; Chandrasekaran, Sowmya; Rehbach, Frederik; Zaefferer, Martin (2022): Case Study I: Tuning Random Forest (Ranger). In: Bartz, Eva; Bartz-Beielstein, Thomas; Zaefferer, Martin; Mersmann, Olaf (Hrsg.): Hyperparameter Tuning for Machine and Deep Learning with R : A Practical Guide. Singapore: Springer Nature, S. 187 - 220. (peer-reviewed/Open Access)
- Bartz-Beielstein, Thomas (2022): Hyperparameter Tuning and Optimization Applications. In: Bartz, Eva; Bartz-Beielstein, Thomas; Zaefferer, Martin; Mersmann, Olaf (Hrsg.): Hyperparameter Tuning for Machine and Deep Learning with R : A Practical Guide. Singapore: Springer Nature, S. 165 - 175. (peer-reviewed/Open Access)