Statistics 598M - Spring 2003
Statistical Data Mining
Course Outline
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An Overview of Data Mining
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Statistical Issuses in High Dimensional Data Analysis
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Statistical Learning Theory
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Basic Statistical Regression and Classification Methods
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Trees and Related Methods
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Bagging and Model Averaging
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Boosting
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Neural Networks
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Support Vectors Machines
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Bayesian Networks
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Association Rules
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Clustering in High Dimensional Space
Books
Textbook:
"The Elements of Statistical Learning: Data Mining, Inference and
Prediction", by Hastie, Tibshirani and Friedman (Springer, 2001).
Reference books:
"Learning From Data: Concepts, Theory and Methods", by Cherkassky and
Mulier (Wiley, 1998).
"Data Mining: Concepts and Techniques", by Han and Kamber (Kaufmann,
2001).
Assessment and Evaluation
Group projects and presentations
Students need to form groups that consist of three students by the end
of Week 3. There will be two to three small projects and one term
project. Each group need hand in the project reports and is required
to give a presentation about its term project at the end of the semester.
The Small projects account for 40% of the final grade and the
term project accounts for 50%
Participation in class is worth the left 10%
SAS Enterprise Miner
This is the first time SAS Enterprise Miner is available on campus. It
is used majorly as a demonstration of how data mining can be performed
in real applications. Due to the limited computing capacity in the
statistics department, students outside the department need apply for
an account from the Krannert computing lab in order to use Enterprise
Miner. Detailed information will be available soon.
Office hours
My office is 510, MATH, phone is 4-6038. Office hours: M1:30-2:30pm,
W1:30-2:30pm and F1:30-2:30. Questions by e-mail are also welcome: yuzhu@stat.purdue.edu
Time and Location
- MWF, 11:30-12:30, MATH 211
Prerequisites
Regression, Multivariate Analysis, Programming Languages C, FORTRAN,
Matlab, R, Splus, etc.
Michael Zhu -
<yuzhu@stat.purdue.edu>
Last modified on 01/12/03