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 |
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Instructors
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Vittorio
Castelli
vittorio@us.ibm.com
(914) 945-2396
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Mark Brodie
mbrodie@us.ibm.com
(914)784-7484
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Accomplices
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Irina Rish
rish@us.ibm.com
(914)784-7431
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Daniel Oblinger
oblinger@us.ibm.com
(914) 945-2326
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Grader
Joseph Yangi Yun
yy49@columbia.edu Office: Mudd 244A
(Phone) 212-854-3155 |
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!
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
on-line |
| 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
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