
ELEN E6880
Spring 2002
Statistical Pattern Recognition

Homework Sets
Homework #1
Homework #2
Homework #3
Homework #4
Homework #5
Project Home Page
Teams for the Project
Lectures
The material for the lectures include foils, lecture
notes, links to additional material,
and references.
1. Probability
2. Introduction
3. Linear Discriminants
4. Neural Networks
5. Parametric Methods
6. Kernel Methods
7. Feature Selection
8. Nearest Neighbor
9. Tree Classifiers
10. SVMs
11. Boosting, etc. .
12. Bayesian Nets
13. Unsup. Learning
14. Comparing 
The class meets Wednesday, 6:50  9:20 PM, in Mudd 545.
Office Hours with the instructors are
Wednesday, 5:506:50 PM, in Mudd 1319.
Office Hours with our grader
are on Thursday, 10 AM12 PM, in Mudd 244A.
Do not hesitate to contact us via email, should
you have questions!
Course
Information (Courtesy of CVN), Including Time Table
Announcements
04/26/2002  New course material
04/26/02 
Unsupervised Learning page updated,
with relevant chapters and bibliography. 
04/26/02 
The slides for the Bayes Network lecture are
online 
04/22/02 
Unsupervised Learning Lecture Notes Available 
04/19/02 
Homework set nr 5
is now available 
04/12/02 
A paper with material for Lecture
12 is available in the additional material 
Useful Links


Grading Policy
There are 4 components to the grade for the class

Homework: 30%

Midterm: 25%

Final:
35%

Project:
15% for solving the first part, 10% for solving the second part
Note that the overall grade without the second part
of the project is 105%, namely, the project gives student 5% bonus points.
Students desiring to do additional work to improve their grade are encouraged
to try part 2 of the project. The grading policy for this second
part of the project is going to be more strict than the first part: in
other words, only students who are really serious about the additional
work should undertake it. 
