Day 1 26.7. Ethical and normative aspects of Trust in ML
9:00 Welcome and Intro (Michael Resch)
9:30 Concepts of Trust (Andreas Kaminski) [Slides]
11:00 Coffee break
11:30 A philosophical perspective on Trust in ML (Andreas Kaminski) [Slides]
13:00 Lunch break
14:30 Trust in ML a practical approach – the VCIO framework (Sebastian Hallensleben) [Slides]
16:00 Coffee break
16:30 Participant presentations
18:30 End
Day 2 27.7. Technical Aspects of ML (Hands-on Session)
All Materials for Day 2 can be found here https://fs.hlrs.de/projects/par/events/2023/sst/
9:00 Introduction to the training cluster (Lorenzo Zanon)
9:30 Pre-processing, Feature Engineering and Machine Learning 1 (Lorenzo Zanon)
11:30 Coffee break
11:45 Can we use deep learning for a small dataset? 1 (Khatuna Kakhiani)
13:00 Lunch break
14:30 Can we use deep learning for a small dataset? 2 (Khatuna Kakhiani)
15:45 Coffee break
16:00 Natural Language Processing (Layal Ali)
18:00 End
Day 3 28.7. Epistemic aspects of Trust in ML
9:00 Epistemology and theory of ML: Impossibility results (Tom Sterkenburg) [Slides]
10:30 Coffee break
11:00 Epistemology and theory of ML: Statistical learning theory and beyond (Tom Sterkenburg) [Slides]
12:30 Lunch break
13:15 Evaluation
14:00 Standards of error and accuracy (Nic Fillion) [Slides]
15:30 Coffee break
16:00 The epistemology of computational error (Nic Fillion) [Slides]
17:30 End