Teaching

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Note: Over TUMOnline

Participants have the necessary methodological and interdisciplinary skills to investigate an advanced topic in computer science, while explaining it in the form of presentations/tutorials, discussions, and an essay. The students can work with scientific literature (i.e. search, categorize, prioritize, cite, ...). They master the required presentation and discussion techniques.

- Independent assessment of an advanced scientific theme
- Preparation of a term paper with a section on related work
- Presentation and discussion of scientific results

First 3 terms of the Bachelor's Program

Type of Assessment: Research Investigation
Before the start of the seminar, the participants select an advanced topic to investigate from a list of provided topics. The selected topic is investigated incrementally, and seminar participants expose their results as a series of tutorials/presentations (in oral form, supported by visual media such as slides) spaced out during the semester. At the end of the semester, the participants prepare a brief essay about the topic. The investigation of a topic is conducted and presented in groups of two participants. Each presentation is followed up by questions and discussions where all seminar participants must be actively involved.

The seminar is evaluated individually based on several evaluation criteria. Special focus is given to how the student responds to questions, suggestions, and discussions. Also, the discussion on the work and presentations of other participants is an important aspect of the evaluation. By that, students demonstrate their expertise for critical analysis of presented scientific contents.
Before the start of the seminar, the respective lecturer will announce how the various evaluation criteria are weighted for the calculation of the module grade. After the end of the semester, the respective lecturer offers an individual feedback session to the seminar participants on their performance.

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Scientific publications to the given topic. Potential topics include:
- Creating knowledge graphs from (semi-)structured or unstructured sources
- Representing facts in knowledge graphs: RDF, RDFS, OWL, Property Graphs
- Querying knowledge graphs: SPARQL, CypherQL
- Knowledge graph quality
- Graph Neural Networks over knowledge graphs
- Knowledge graph embeddings