Pattern Recognition

Catalog description

Credits 3. 3 Lecture Hours. Optimal classification; parametric and nonparametric classification; support vector machines; neural networks; decision trees; error estimation; dimensionality reduction; model selection; Vapnik-Chervonenkis theory; least-squares regression; Gaussian process regression. Prerequisite: Graduate classification; undergraduate probability theory and python programming skills; or approval of instructor.