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Prof. Dr. Olaf Mersmann

Dr. rer. nat.
Faculty of Computer Science and Engineering Science

Prof. Dr. Olaf Mersmann

Technische Hochschule Köln
Steinmüllerallee 6
51643 Gummersbach
Room 1.503 Mailing address


  • Phone: +49 2261-8196-6564

Office hours

Monday, 16:00 to 17:00
Campus Gummersbach, Room LC6 1.503
Between semesters, only by appointment.

Teaching disciplines

  • Data Science & Ethics (Master Data Science)
  • Data Mining (Bachelor Informatik, Master Medieninformatik)

Research fields

  • Exploratory Landscape Analysis
  • Benchmarking
  • Machine Learning
  • 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)

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