Machine Learning & Pattern Recognition

Catalog description

This graduate course covers some fundamental theoretical concepts in machine learning, and common patterns for implementing methods in practice. The intended audience is those wanting the background required to begin research and development of machine learning methods. The course provides foundations of pattern recognition algorithms and machines, including statistical and structural methods. Data structures for pattern representation, feature discovery and selection, classification vs. description, parametric and non- parametric classification, supervised and unsupervised learning, use of contextual evidence, clustering, recognition with strings, and small sample-size problems. Three credits; one-hour laboratory.