Kombination von Operations Research und Data Science

By the end of the course, students will be able to analyze and solve complex decision-making and optimization problems under uncertainty. They will be capable of selecting, combining, and adapting appropriate methods from Operations Research, Data Science, and Machine Learning. Additionally, they will be able to identify, evaluate, and apply suitable optimization algorithms to real-world problem settings.

Written exam, 90 min.

• Santos, H.G., Toffolo, T.A.M., Silva, R.M., & Resende, M.G.C. Mixed Integer Linear Programming with Python . Retrieved from https://app.readthedocs.org/projects/python-mip/downloads/pdf/latest/
• Birge, J.R., & Louveaux, F. (1999). An introductory tutorial on stochastic linear programming models. Interfaces , 29(2), 33–44. https://doi.org/10.1287/inte.29.2.33
• Hutter, F., Kotthoff, L., & Vanschoren, J. (2018). Model evaluation, model selection, and algorithm selection in machine learning. arXiv preprint arXiv:1811.12808 . Retrieved from https://arxiv.org/pdf/1811.12808

• A new set of tasks (including formulating and implementing mathematical optimization models, writing and executing python code for simple machine learning tasks, etc) will be assigned each week for students to work on independently at home
• These homework tasks are not mandatory , but students are strongly encouraged to attempt them
• During the exercise sessions , selected homework problems will be discussed and solved collaboratively
• Students who present solutions (oral presentation) to homework problems during these sessions will earn bonus points

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