PHYS 546 - Pattern Recognition

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Description

Hours: Three hours lecture in the lab per week. Prerequisites: 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.

Units: 3.00
Grading: Letter Grade