🎓 About the Rough Set School
The Rough Set School is an educational tutorial program organized to provide graduate students, early-career researchers, and industry practitioners with a solid foundational and practical understanding of rough sets, granular computing, and uncertainty reasoning.
Key Topics & Goals:
- Introduce the fundamental mathematical principles of rough sets and approximation spaces.
- Explore practical methodologies for feature selection, rule extraction, and classification under incomplete and uncertain data.
- Bridge the gap between theoretical foundations and real-world deployment in contemporary data science, artificial intelligence, and machine learning.
- Provide a collaborative learning environment for networking with pioneering researchers and international peers.
💡 Call for Tutorial Proposals
As part of the Rough Set School, we cordially invite experts, educators, and senior researchers to submit proposals for tutorials. Topics may cover the theory and applications of artificial intelligence, data mining, machine learning, and uncertainty modeling.
How to Submit a Proposal:
Tutorial proposals should be submitted via email directly to the Rough Set School Chairs and the organizing committee:
✉ Email proposals to: ijcrs2027@cs.umanitoba.ca
Rough Set School Chairs:
University of Milano-Bicocca, Italy
University of Catania, Italy