CS 4803B/8803B
Pattern Recongition
Spring 2000
College of Computing 101
MW 4:30-6:00
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
- 218 CCB
- (404)894-8591
- Office Hours: Drop by or send email to schedule an appointment.
- Teaching Assistant
- Dong Song
- dsong@cc.gatech.edu
- Office: CCB 153
- (404)894-1155
- Office Hours: Mon and Wed, 11am-12noon, CoC common area
-
-
-
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 and Scene Analysis
bu Duda, Hart, and Stork. This is the second additiona of the text,
but it is not in print yet and therefore will be distributed
as handouts.
The lectures for this class are being captured. To find them go
to eClass index and
click on Pattern Recognition.
Some extra readings
The Cui and Weng paper is here.
Problem sets
Problem Set 1: Due Jan 26 The postscript for the PS is here. Solutions are hardcopy for the moment.
Problem Set 2: Due Feb 16 The postscript for the PS 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 March 1 The postscript for the PS is here.
Problem Set 4: Due March 29 The postscript for the PS 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.
Problem Set 5: Due April 5 The postscript for the PS is here. The ascii data sets for problem 1 are the
same as before, but to keep things simple they are also class1, class2, and class3. These can just be downloaded
to a local ascii copy.
There will be bi-weekly problem sets that will invlove some math and
some Matlab. There will also be a single comprehensive exam (a late
midterm) and a final project.
Collaboration on problem sets is encouraged at the "white board
interaction" level.
All problem sets should be in on time. Two late problem sets are
accepted without excuse presuming handed in before the solutions are
distributed.
Grading
- Undegrads (4803B): Problem sets, 50%; Midterm, 25%; Final Project 15%;
Participation, at least 10%.
- Grad students: Problem sets, 40%; Midterm, 20%; Final Project 30%;
Participation, at least 10%.
Contact Information:
Aaron Bobick
afb@cc.gatech.edu
College of Computing
Georgia Institute of Technology
Atlanta, GA 30332-0280