MATH 546 - Pattern Recognition

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.

Meeting Information

Status: Open
Class Number: 2813
Session: Extended Session 1
Units: 3.00
Class Components: Lecture
Career: Postbaccalaureate
Dates: 1/23/2021 - 5/28/2021
Grading: Letter Grade

Class Availability

Information below is 24 hours old.
Enrollment Total: 11
Available Seats: 9
Wait List Capacity: 10
Wait List Total: 0

Enrollment Information

• Graduate Division

Notes