Unsupervised Learning (Part 2)

Abstract: The second session delves into clustering methods and other techniques for uncovering latent structure in data without relying on response variables. We cover K-means clustering and Hierarchical clustering, including how they work, how to choose the number of clusters, how to decide on distance metrics, and practical pitfalls (e.g. scaling, sensitivity to initialization). We discuss how to interpret clusters, validate clustering solutions, and when unsupervised grouping might be appropriate.

Computer scienceMathematics

Audience: general audience

Basic Notions and Applied Topology Seminar