CS 4803B/8803B
Pattern Recongition

Spring 2001
College of Computing, CCB 102
Tues. and Thurs. 3:00-4:30

Description
This course introduces techniques for Pattern Recognition. The course presumes a reasonable background in probablility and linear algebra. The syllabus includes basic PR including Bayesian decision and estimation, non-parametric methods, multi-class classifiers, eigenvector and other feature selection methods, and EM techniques. Time permitting we will also cover additional topics of interest including sequence analysis via HMMs and support vector machines.

Instructor
Aaron Bobick
afb@cc.gatech.edu
253 CCB (temporary)
(404)894-8591 (never pick it up - email much better...)
Office Hours: For now, drop by or send email to schedule an appointment.
Teaching Assistant
None yet....
Course Administrator
Melissa O'Malley
omalley@cc.gatech.edu
GVU Office, CCB 244 CCB (temporary)
(404)894-4666

General Information

This course will teach you the basic techniques of Pattern Recognition. By the end of the cousre you should be able to implement a pretty standard PR system, and also have enough basis to understand more complex approaches.

The text for the class is Pattern Classification by Duda, Hart, and Stork. This is the second edition of the text, and should be in the bookstore.

The lectures for this class are being captured. To find them go to this eclass page.

Requirements, Collaboration, and Grading

The grades will be assessed as follows:

Problem Sets (not all PS are created equal)

50%

Mid-term

20%

Final project

20%

Class Participation

10%

There will be 5 or so bi-weekly problem sets that will involve some Matlab and hopefully some thinking. Collaboration on problem sets is encouraged at the "white board interaction" level. That is, share ideas and technical conversation, but write your own code. All problem sets should be in on time. One late problem set is accepted late (but before the next one is due) without excuse. After that, get prior permission.

There will be a "mid-term" somewhere around 2/3 the way through, just to make sure we're all on the same page.

There will be occasional readings assigned that will typically cover technical material that you may be responsible for.

Undergrads and grads will be graded on separate curves; more is expected from a graduate project than an undergraduate project.

Approximate Syllabus

This course is designed to be an senior level or first year or two grad student course covering basic pattern recognition, plus some more modern techniques. The goal is that by the end of the semester you know enough Pattern Recognition to be dangerous: can attempt a real problem but probably need to dig deeper to handle a real example. Also, you will have exposure to some more current approaches that are currently being researched. Should allow you to take a more advanced PR or machine learning course. When possible we will use examples from computer vision, something close to my heart.

Some extra readings

There will be additional required readings. When they are available electronically you will be able to find them here:

Problem sets

Problem Set 1: Due Jan 23 The postscript for the PS is here and the PDF file is here.

Problem Set 2: Due Feb 13 The PDF file is here. The data set of question 4 is here but since it's binary data you need to right click (or ctl-click for you die hard mac types) and select "Download link to disk".

Problem Set 3: Due Feb 13 The PDF file is here.

Problem Set 4: Due April 5 The PDF is here. The data sets of question 1 are training and testing but since it's binary data you need to right click (or ctl-click for you die hard mac types) and select "Download link to disk". The ascii data sets for problem 3 are class1, class2, and class3. These can just be downloaded to a local ascii copy.

Project data

For those without their own project data, here are some to play with. All these files are ASCII (in case not using MATLAB) so you should just down load them and then load with a command such as LOAD easy.ascii The files are the easy data, some slightly harder data, and labels. The data are 4 rows of 40 dimensional data for each of 25 subjects (data matrices are 100 rows by 40 columns). See how well you do by leave one out, or some other test.

Contact Information:

Aaron Bobick
afb@cc.gatech.edu
College of Computing
Georgia Institute of Technology
Atlanta, GA 30332-0280
Tel: 404-894-8591
email: afb@cc.gatech.edu