University of Florida

Department of Electrical and Computer Engineering

EEL 6825, Section 3953 

Pattern Recognition

Spring 2013

Course Description

This is a 3-credit course.

The objective of this course is to impart a working knowledge of several important and widely used pattern recognition topics to the students through a mixture of motivational applications and theory. 

Course Prerequisites

Required Textbook

Recommended Readings


Dr. Dapeng Wu
Office: NEB 431


Baohua Sun

Course website:

Meeting Time

Monday, Wednesday, Friday, period 8 (3 pm - 3:50 pm)    

Meeting Room

NEB 201

Office Hours

Structure of the Course

The course consists of lectures, 4 homework assignments, and 1 project.

This course is primarily a lecture course.   I cover all important material in lectures.  Since EEL 3135 and  EEL 4516 are  prerequisites, I assume some previous knowledge about DSP, probability theory and stochastic processes, and hence I will cover some material very quickly.  Thus, depending on what and how much you recall from earlier study, varying amounts of reading in introductory books on DSP, probability theory and stochastic processes (other than the course textbook) may be necessary; these readings are up to the student.  I will only give reading assignments from the course textbook.

Attending lecture is quite important as I may cover material not available in any book easily accessible to you. I use Powerpoint presentation during lecture.  Lecture notes will be posted on the course website before the class.  The lecture is to engage the students in independent thinking, critical thinking, and creative thinking, help the students organize the knowledge around essential concepts and fundamental principles, and develop conditionalized knowledge which tells them when, where and why a certain method is applicable to solving the problem they encounter.  

I do not intend for the WWW material to be a substitute for attending lecture since engaging the students in active thinking, making logical connections between the old knowledge and the new knowledge, and providing insights are the objectives of my lecture.   The lecture notes are posted on the web so that you can miss an occasional lecture and still catch up, and it makes taking notes easier.  To reward those who attend regularly, there will be some lecture-based material in the exam which is not available via the web.

The class project is described here.

Course Outline

Course Objectives

Upon the completion of the course, the student should be able to


Please find handouts here.

Course Policies

Useful links:


Grades Percentage Dates
Homework 30% TBA
Project proposal 10% 4pm, March 15
Project report 60% 4pm, May 1

The project report consists of

  1. (50%) A written report for your project
  2. (25%) Computer programs that you develop for your project
  3. (10%) Powerpoint file of your presentation
  4. (15%) Your presentation/demo video on YouTube

Grading scale:

Top 25% students will receive A. Average score will be at least B+.


Class Project:

The class project will be done individually (that is, teaming with other students is not allowed).   Each project requires a proposal and a final report.   The final report is expected to be in the format of a conference paper plus computer programs, a Powerpoint file, and a video.   On March 15, the project proposal (up to 2 pages) is due.  On May 1, the final report (up to 10 pages) is due.   For details about the project, please read here.

Suggested topics for projects are listed here.

Course calendar can be found here.

Useful links





Computer Vision

Public Domain Image Databases

CMU Database