Data Mining: A Mathematical Perspective
Mathematical and statistical aspects of data mining. Topics include supervised learning (regression, classification, support vector machines) and unsupervised learning (clustering, principal components analysis, dimensionality reduction). Uses technical tools that draw from linear algebra, multivariate statistics, and optimization. Computer Science 391D and 395T (Topic: Data Mining: A Statistical Learning Perspective) may not both be counted. Three lecture hours a week for one semester.
Topics
- supervised learning (regression
- classification
- support vector machines) and unsupervised learning (clustering
- principal components analysis
- dimensionality reduction)
Topics explicitly listed in official catalogue description
Prerequisites & course information
Mathematical and statistical aspects of data mining. Topics include supervised learning (regression, classification, support vector machines) and unsupervised learning (clustering, principal components analysis, dimensionality reduction). Uses technical tools that draw from linear algebra, multivariate statistics, and optimization. Computer Science 391D and 395T (Topic: Data Mining: A Statistical Learning Perspective) may not both be counted. Three lecture hours a week for one semester.
Sources
- Course catalogue ↗2026-2027