CSC 577 - Pattern Recognition
Course Description: This course introduces pattern recognition methods and theory using conventional statistical approaches, neural networks, fuzzy logic, support vectors, and linear principal component analysis (PCA). The course also presents methods for non-linear PCA, clustering, and feature extraction. Students implement algorithms; apply methods to selected problems, and to document findings.
Credit Hours: 3
Corequisite Courses: None
Prerequisite Courses: CSC 340 with minimum grade of C
Additional Restrictions/ Requirements: Prerequisite course or equivalent
Course Repeatability Course may not be repeated
ADDITIONAL COURSE INFORMATION
Equivalent Courses: None
Undergraduate Crosslisting: None
Additional Course Fees: None
Course Attribute: None
Click here for the Spring 2023 Class Schedule.