We invite submissions on all aspects of data mining, knowledge discovery, and machine learning. In addition to original research, we also invite resubmissions of recently published articles at major conference venues related to KDML. Moreover, KDML explicitly invites student submissions.

Topics of interest include but are not limited to:

1. Foundations, algorithms, models, and theory of machine learning and data mining
2. Supervised, semi-supervised, and unsupervised learning
3. Machine learning on networks and graphs
4. Deep learning and representation learning
5. Rule-based learning and pattern mining
6. Reinforcement learning
7. Fairness, transparency and formal guarantees in machine learning
8. Explainability and Interpretability in machine learning and knowledge discovery
9. Trustworthiness in machine learning and knowledge discovery
10. Temporal, spatial & spatio-temporal data analytics
11. Online learning and machine learning in data streams
12. Text mining, mining unstructured and semi-structured data
13. Parallel and distributed data analytics
14. Interactive and visual analytics
15. Applications of data mining and machine learning in all domains including natural-,  life-, and social sciences, health, financial, environment, engineering, and humanities
16. Open source frameworks and tools for data mining and machine learning

Workshop Organisation

Felix Stamm, Rheinisch-Westfälische Technische Hochschule Aachen

Dr. Helge Spieker, Simula Research Laboratory, Oslo, Norway