Patterns and Surprises in Rich but Noisy Network Data

This course aims to teach students how to accurately estimate network structure from noisy but rich data, focusing on social and biological examples. The course covers topics such as social networks, network structure, expectation maximization, clustering coefficient, and the EM algorithm. The teaching method involves a seminar format with a focus on real-world examples and applications. The intended audience for this course includes researchers, data scientists, and individuals interested in network analysis and data science.