The recent surge of Machine Learning (ML) has opened up various opportunities when analysing biological datasets. Graph Neural Networks (GNNs) are a fairly new deep learning model capable of handling biological data in the best way overall.
The seminar will start with a few (around 4 or 5) introductory lectures on Graph Representation Learning basics and Graph Neural Networks. The earliest and most recent approaches will be discussed, together with their use cases and drawbacks. The initial set of lectures will be followed by two lectures in which it will be presented how to write technical reports and how to prepare a good presentation.
Then seminar presentations will take place, and they will need to be presented in small groups of 1-2 students.
The course will be seminar+tutorial style, thus students will: 1. present a chosen paper; 2. deliver a final report of around 10 pages; 3. weekly deliver a summary of the presentation that took place during that week (around 500 words long).
The course will is entirely held in English.
Frequency | Weekday | Time | Format / Place | Period | |
---|---|---|---|---|---|
weekly | Di | 16-18 | V2-105/115 | 07.04.-18.07.2025 |
Module | Course | Requirements | |
---|---|---|---|
39-Inf-BDS Biomedical Data Science for Modern Healthcare Technology | Ausgewähltes Seminar oder Projekt | Study requirement
|
Student information |
39-Inf-WP-CLS-x Computational Life Sciences (Schwerpunkt) | Vertiefendes Seminar | Student information | |
- | Graded examination | Student information | |
39-Inf-WP-DS-x Data Science (Schwerpunkt) | Vertiefendes Seminar | Student information | |
- | Graded examination | Student information | |
39-M-Inf-AI-app Applied Artificial Intelligence | Applied Artificial Intelligence: Seminar | Student information | |
- | Graded examination | Student information |
The binding module descriptions contain further information, including specifications on the "types of assignments" students need to complete. In cases where a module description mentions more than one kind of assignment, the respective member of the teaching staff will decide which task(s) they assign the students.