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Module Number:
| 15006
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| Module Title: | Research Seminar: Recent Advances in Operations Research and Machine Learning |
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Forschungsseminar: Aktuelle Entwicklungen im Operations Research und im Maschinellen Lernen
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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: | After completing the modul students are be able to: - Read and critically analyze research papers in
Combinatorial Optimization, Operations Research,
and Machine Learning - Explain problem formulations, assumptions, and
contributions clearly - Evaluate strengths and limitations of approaches in
papers - Present technical content in a structured way
- Formulate, propose and justify potential research
extensions or new ideas - Participate in scientific discussion
- Getting prepared for Master’s thesis
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| Contents: | - Basics of approaches in Combinatorial Optimization,
operations research, and machine learning - Research methods in Combinatorial Optimization,
operations research, and machine learning - Scientific paper reading and analysis
- Problem formulation and literature positioning
- Algorithmic ideas, proofs, and experiment evaluation
- Research proposal development
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| Recommended Prerequisites: | - Basic knowledge of optimization, algorithms and discrete mathematics; familiarity with machine learning or AI; at least one programming language
- Knowledge of the content of one of the modules
- 14495 Optimization in Business Transformation OR
- 14731 Combining Operations Research and Data Science
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| Mandatory Prerequisites: | no successful participation in module - 14060 Research Module in Artificial Intelligence
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| Forms of Teaching and Proportion: | -
Exercise
/ 2 Hours per Week per Semester
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Seminar
/ 2 Hours per Week per Semester
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Self organised studies
/ 120 Hours
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| Teaching Materials and Literature: | - Introduction to Operations Research, Hillier, F. S. & Lieberman, G. J.
- Dynamic Programming and Optimal Control, Bertsekas, D. P.
- further materials via Moodle (will be announced during the first week of the semester)
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| Module Examination: | Continuous Assessment (MCA) |
| Assessment Mode for Module Examination: | - 3 paper presentations: , each 15 min. and includes 8-15 slides (50%)
- 2 summaries, 1500-2000 words (20%)
- Final implementation (algorithm development (a detailed pseudocode) and a running code with no errors that produces solutions/results. (30%)
- Bonus: Active participation and discussion - up to 10 % (is added to your final score only if you pass the course)
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| Evaluation of Module Examination: | Performance Verification – graded |
| Limited Number of Participants: | 10 |
| Part of the Study Programme: | -
Master (research-oriented) /
Artificial Intelligence /
PO 2022
- 1. SÄ 2024
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Master (research-oriented) /
Mathematical Data Science /
PO 2025
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Master (research-oriented) /
Transformation Studies /
PO 2024
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| Remarks: | Module with limited number of participants - Registration two weeks prior to the commencement of lectures! |
| Module Components: | None |
| Components to be offered in the Current Semester: | |
