Statistics 598M - Spring 2003

Statistical Data Mining


Course Outline

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

    Prerequisites

    Regression, Multivariate Analysis, Programming Languages C, FORTRAN, Matlab, R, Splus, etc.


    Michael Zhu - <yuzhu@stat.purdue.edu>

    Last modified on 01/12/03