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

Info current as 12/22/2024
Days Times Room Meeting Dates Instructor
TH  7:30 PM  -  10:00 PM  Online 1/22/2022 - 5/20/2022 William Barber

Status: Open
Class Number: 2731
Session: Extended Session 1
Units: 3.00
Class Components: Lecture
Career: Postbaccalaureate
Dates: 1/22/2022 - 5/20/2022
Grading: Letter Grade

Class Availability

Information below is 24 hours old.
Enrollment Total: 19
Available Seats: 5
Wait List Capacity: 5
Wait List Total: 0

Enrollment Information

  • Graduate Division
  • Synchronous online

Notes

Cross-listed course