Schlichtig, Michael, et al. “Understanding Artificial Intelligence – A Project for the Development of Comprehensive Teaching Material.” ISSEP 2019 - 12th International Conference on Informatics in Schools: Situation, Evaluation and Perspectives, Local Proceedings, edited by Egl Jasut and Sergei Pozdniakov, vol. 12, 2019, pp. 65–73.

Casalicchio, Giuseppe, et al. “OpenML: An R Package to Connect to the Machine Learning Platform OpenML.”10.1007/s00180-017-0742-2__Computational Statistics, vol. 34, no. 3, 2019, pp. 977–991, doi:10.1007/s00180-017-0742-2.

Wever, Marcel Dominik, et al. “ML-Plan for Unlimited-Length Machine Learning Pipelines.” ICML 2018 AutoML Workshop, 2018.

Kim, Yeongsu, et al. “Improving Classifiers for Semantic Annotation of Software Requirements with Elaborate Syntactic Structure.”10.14257/ijast.2018.112.12__International Journal of Advanced Science and Technology, vol. 112, SERSC Australia, 2018, pp. 123–36, doi:10.14257/ijast.2018.112.12.

Kerschke, Pascal, et al. “Leveraging TSP Solver Complementarity through Machine Learning.”10.1162/evco_a_00215__Evolutionary Computation, vol. 26, no. 4, 2018, pp. 597–620, doi:10.1162/evco_a_00215.

Kim, Yeong-Su, et al. “Semantic Annotation of Software Requirements with Language Frame.” International Journal of Software Engineering for Smart Device, vol. 4, no. 2, Global Vision School Publication, 2017, pp. 1–6.

Bischl, Bernd, et al. “Algorithm Selection Based on Exploratory Landscape Analysis and Cost-Sensitive Learning.”10.1145/2330163.2330209__Proceedings of the 14th Annual Conference on Genetic and Evolutionary Computation, Association for Computing Machinery, 2012, pp. 313–320, doi:10.1145/2330163.2330209.