Statistical Pattern Recognition
Project Home Page
Teams for the Project
The material for the lectures include foils, lecture
notes, links to additional material,
3. Linear Discriminants
4. Neural Networks
5. Parametric Methods
6. Kernel Methods
7. Feature Selection
8. Nearest Neighbor
9. Tree Classifiers
11. Boosting, etc. .
12. Bayesian Nets
13. Unsup. Learning
The class meets Wednesday, 6:50 - 9:20 PM, in Mudd 545.
Office Hours with the instructors are
Wednesday, 5:50-6:50 PM, in Mudd 1319.
Office Hours with our grader
are on Thursday, 10 AM-12 PM, in Mudd 244A.
Do not hesitate to contact us via e-mail, should
you have questions!
Information (Courtesy of CVN), Including Time Table
04/26/2002 - New course material
||Unsupervised Learning page updated,
with relevant chapters and bibliography.
||The slides for the Bayes Network lecture are
||Unsupervised Learning Lecture Notes Available
||Homework set nr 5
is now available
||A paper with material for Lecture
12 is available in the additional material
There are 4 components to the grade for the class
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.
15% for solving the first part, 10% for solving the second part