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Class Links
CVN
page for the class
Project Home Page
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... |
Instructor
Vittorio
Castelli
vittorio@us.ibm.com
(914) 945-2396
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The class was taught in collaboration
with
Mark Brodie
(the other official instructor)
mbrodie@us.ibm.com
(914)784-7484
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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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Announcements
| 05/29/03 |
Most of the material for lecture 1 is more
advanced than what you need for the class - do not get scared by it! |
Useful Links
On-Line
Resources
Glossary
of Machine Learning-Statistical Classification Terms
First
8 chapters of the Book |
Grading Policy
There are 4 components to the grade for the class
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Homework: 30%
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Midterm: 25%
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Final:
35%
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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. |
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