Class Number: 2350
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.
| Days | Time | Date Range | Location | Instructor |
|---|---|---|---|---|
| TH | 06:00 PM - 09:00 PM | 01/26/2014 - 05/16/2014 | Aliso Hall 133 | Geoff Dougherty |
Status: Open
Session: Extended Session 1
Units: 3.00
Class Components: Lecture
Career: Postbaccalaureate
Start Date: 01/26/2014
End Date: 05/16/2014
Grading: Letter Grade
Class Availability
Information below is 24 hours old.Enrollment Total: 11
Available Seats: 9
Wait List Capacity: 20
Wait List Total: 0
Textbook / Other Materials
Textbook Status: Required
ISBN: 9781461453222
Title: Pattern Recognition and Classification: An Intro.
Author: G. Dougherty
Publish: Spring NY Pub.
More textbook information including prices
Enrollment Information
- Graduate Division
Notes
- MS MATH
- Cross-listed course