Introduction to Pattern Recognition
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, the use of contextual evidence, clustering, and small sample-size problems. Programming projects will include handling of pictorial and textual patterns.
None presently available.
This course does not fulfill core area (depth) or core course (breadth) requirements.
2 years of college mathematics, including probability theory.
|Spring 2008||LR||Pattern Recognition||Dr. Jason J. Corso||3||0/ 0|
|Fall 2002||LR||Pattern Recognition||Dr. Sargur (Hari) N. Srihari||3||16/40|
|Fall 1999||LR||Pattern Recognition||Dr. Sargur (Hari) N. Srihari||3||18/30|