Abstract: Supervised learning with statistical tools is the dominant approach in contemporary AI applications. However, one can not always come up with rich datasets desperately needed to deploy supervised learning algorithms. Network analysis provides a versatile framework for unsupervised learning. We will cover basics of network analysis, centrality measures, community detection algorithms and their applications to practical matters.

Speaker Biography: Salih Durhan received his PhD in mathematics from UIUC in 2007. He has worked mainly in abstract algebra and model theory as an academic. His research on theoretical artificial intelligence started in 2012, and later transformed into applied AI. He held academic positions in McMaster University, METU NCC and Nesin Math Village until 2018. In 2018, he co-founded D4C, providing data oriented solutions to businesses.

Webpage: https://www.researchgate.net/scientific-contributions/2030989989_Salih_Durhan