EEL6825: Syllabus

Instructor: John G. Harris
Office: CSE-446
Phone: x2-2652
Email: harris@cnel.ufl.edu
Office hours: Tues&Thurs 2-3 period (8:30-10:25)

Prerequisite: The course assumes previous programming experience and some familiarity with linear algebra and statistics.

Class Meeting: Tue 8-9 period (3:00-4:55), Thu 8th period (3:00-3:50), CSE-118

Required textbook: Nadler & Smith, Pattern Recognition Engineering, John Wiley, 1993.

Reference Books:

• Pattern Classification and Scene Analysis by Richard Duda and Peter Hart, John Wiley & Sons 1973.
• Introduction to Statistical Pattern Recognition by Keinosuke Fukunaga, Academic Press 1990.

Course Objectives: This class provides an introduction to classical pattern recognition. Pattern recognition is the assignment of a physical object or event to one of several prescribed categories. Applications include automated speech recognition, fingerprint identification and optical character recognition. Homework assignments will include computer assignments using MATLAB. Grades are based on the results of two in-class exams as well as on performance on homework assignments. The major course topics include:

1. Bayesian decision theory
2. Parametric estimation and supervised learning
3. Linear discriminant functions
4. Nonparametric methods
5. Feature extraction for representation
6. Feature extraction for classification
7. Unsupervised learning and clustering