Pattern Analysis

Catalog description

Credits 3. 3 Lecture Hours. Introduction to methods for the analysis, classification and clustering of high dimensional data in Computer Science applications; includes density and parameter estimation, linear feature extraction, feature subset selection, clustering, Bayesian and geometric classifiers, non-linear dimensionality reduction methods from statistical learning theory and spectral graph theory, Hidden Markov models, and ensemble learning. Prerequisites: MATH 222, MATH 411 or equivalent, and graduate classification.