On April 12h, 2023 the Data Science Initiative organized a symposium on Explainable Machine Learning.
Program
First Session 14:00 – 15:30
14:00 Opening
14:05 Keynote Holzinger et al (Institute of Forest Engineering)
Human-Centered AI for smart forest operations: Group Research topics
14:50 Sangeeta Kumari (Core Facility Bioinformatics):
Network pharmacology: potential target proteins
15:10 Stefan Böhmdorfer (Institute of Chemistry of Renewable Resources):
Turning chemical information more visible: Visualization and automated classification of chromatographic results
Coffee break 15:30 – 16:00
Second Session 16:00 – 17:30
16:00 Mathew Herrnegger (Institute of Hydrology and Water Management):
Machine Learning for modeling and prediction of discharge,
and the examination of the model internals to gain insight into the underlying processes
16:20 Enrico Soranzo (Institute of Geotechnical Engineering):
Genetic programming for the prediction of the tunnel face support pressure
16:40 Martin Riegler (Wood K plus):
Machine learning for wood technology research
17:00 Emma Izquierdo-Verdiguier (Institute of Geomatics):
Feature selection based on random forest for classification and regression tasks in remote sensing
17:20 Discussion
Networking with snacks and drinks 17:30 – open end
