Unsupervised Learning

Catalog description

This course provides an in-depth introduction to unsupervised learning techniques for analyzing and interpreting unlabeled data. Students explore key concepts such as clustering, dimensionality reduction, and anomaly detection, using both traditional and modern approaches. The curriculum emphasizes practical applications across various domains such as market basket analysis, customer segmentation, music genre classification, and fraud detection. Optional topics in graph-based learning and manifold learning allow further exploration of advanced methods used in social network analysis and high-dimensional data visualization. Prerequisite: CS 120 ; MTH 308 ; STAT 305 . Offered Spring.