Description:
The seminar will begin with an overview of several concepts from
statistics and machine learning, including regression, prediction,
descision trees, classification, neural networks, and genetic algorithms.
The second part of the semester will consist of discussions of readings
(case studies) to be led by the participants in the seminar.
Each particpant will be responsible for the following:
Presentation of a chapter from the textbook 20%
Presentation of a research paper (to be approved by Jan 24) 30%
Data Mining project 50%
A final project will also be required involving the development of a
data mining application using available software (such as SPSS or
S-Plus). Use the Link-to-learn spreadsheet
to mine data from. Click here for a description of
the variables.
Some data mining resources on the web (just a fraction of what is out
there):