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Module Number:
| 14731
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| Module Title: | Combining Operations Research and Data Science |
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Kombination von Operations Research und Data Science
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| Department: |
Faculty 5 - Business, Law and Social Sciences
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| Responsible Staff Member: | -
Prof. Dr. rer. pol. Xie, Lin
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| Language of Teaching / Examination: | English |
| Duration: | 1 semester |
| Frequency of Offer: |
Every winter semester
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| Credits: |
6
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| Learning Outcome: | 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. |
| Contents: | - Introductory Case Study, Representing and Computing with Uncertain Quantities
- Obtaining Probability Distributions and Probabilistic Machine Learning
- Decision Making Under Uncertainty, the Value of Information
- Optimization Under Uncertainty I: Two-Stage Stochastic Programming
- Optimization Under Uncertainty II: Chance-Constrained Programming
- Machine Learning for Algorithm Selection
- Algorithm Configuration
Home Assignments:
- 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.
In the Exam students will be required to:
- Provide the mathematical formulation of given problems.
- Write the pseudocode for the solution of some tasks.
- Answer conceptual questions related to the course content.
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| Recommended Prerequisites: | - Basics of Python programming,
- basics of linear programming,
- basics of probability distributions
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| Mandatory Prerequisites: | None |
| Forms of Teaching and Proportion: | -
Lecture
/ 2 Hours per Week per Semester
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Exercise
/ 2 Hours per Week per Semester
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Self organised studies
/ 120 Hours
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| Teaching Materials and Literature: | - 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
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| Module Examination: | Final Module Examination (MAP) |
| Assessment Mode for Module Examination: |
100% exam + bonus points (maximum 10% of the final grade and only valid after passing the exam) |
| Evaluation of Module Examination: | Performance Verification – graded |
| Limited Number of Participants: | 20 |
| Part of the Study Programme: | -
Master (research-oriented) /
Artificial Intelligence /
PO 2022
- 1. SÄ 2024
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Master (research-oriented) /
Informatik /
PO 2008
- 3. SÄ 2024
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Master (research-oriented) /
Mathematical Data Science /
PO 2025
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Master (research-oriented) /
Mathematics /
PO 2025
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| Remarks: | Home Assignments:
- 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.
These bonus points will be added to the final grade, but only if the student passes the final exam |
| Module Components: | |
| Components to be offered in the Current Semester: | |
