Master Seminar "Deep Learning for Graphs" / "Recent Developments in Data Science" (WS 2019/20)
- Contact: Prof. Dr. Thomas Seidl, Max Berrendorf, Evgeniy Faerman
- Required: Successful participation in the lectures "Machine Learning" or "Deep Learning and Artificial Intelligence" or equivalent
Please indicate in the central registration form in uni2work in which semesters you had attended these courses.
- Audience: The lecture is directed towards highly motivated Master students in Mediainformatics, Bioinformatics and Informatics as well as Data Science with strong interest in cutting edge research
- Registration: via uni2work (from Aug 26)
The availability of large amounts of data and increased computational power enabled the renaissance of neural networks as an universal paradigm for machine learning. While initially mainly boosted by advances on image classification, Deep Learning methods recently progress into different domains. One domain of particular interest are graphs, as they allow encoding less regular structures prevalent in numerous application domains, reaching from sensor networks to knowledge graphs.
In this seminar, students will discuss fundamental works in the quickly evolving area of deep learning for graphs, and in particular knowledge graphs, and will venture into the state-of-the-art of this exciting research area.
|We, 16.10.2019||14:15-15:45||131 (Oe 67)||Kickoff Slides|
|We, 11.12.2019||14:15-15:45||131 (Oe 67)||Group Presentations I
|We, 18.12.2019||14:15-15:45||131 (Oe 67)||Group Presentations II
|We, 15.01.2020||14:15-15:45||131 (Oe 67)||
Individual Presentations I
|We, 22.01.2020||14:15-15:45||131 (Oe 67)||
Individual Presentations II
|We, 29.01.2020||14:15-15:45||131 (Oe 67)||
Individual Presentations III
|We, 05.02.2020||14:15-15:45||131 (Oe 67)||Individual Presentations (Backup Slot)|
|Su, 01.03.2020||Seminar Thesis Submission Deadline|