Pattern Recognition (Cross-listed as PHYS 546, MATH 546)

Catalog description

Three hours of lecture in the lab per week Prerequisite(s): Admission to the Computer Science or Mathematics Graduate Program New and emerging applications of pattern recognition (PR) such as data mining, web searching, multimedia data retrieval, face recognition, and cursive handwriting recognition - require robust and efficient techniques. Statistical decision making and estimation are fundamental to the study of PR. Pattern content is analyzed using feature extraction and classification. The principles and concepts underpinning PR, and the evolution, utility and limitations of various techniques (including neural networks) will be studied. Programming exercises will be used to implement examples and applications of PR processes, and their performance on a variety of diverse examples will be studied. Same as: PHYS 546 , MATH 546