EE 380L: DATA MINING
Spring 2007

Class times: TTh: 9:30-11am, ENS 126, Unique No. 16375
Instructor: Joydeep Ghosh. ghosh@ece.utexas.edu; www.lans.ece.utexas.edu/~ghosh
Office: ACES 3.118, 471-8980
Office Hrs: TTh 1:30-2:30pm. Other times by appointment only.
TA info: TBD xx@lans.ece.utexas.edu, office hrs: ACES 3.106

PREREQUISITES: (Graduate standing in ECE, BME, CS or Maths) OR (consent of the instructor). You are expected to know basics (undergraduate level) of probability/statistics. Knowledge of basic linear algebra  will help, but is not required.

COURSE URL: http://www.lans.ece.utexas.edu/courses/ee380l/

COURSE OUTLINE: The information explosion of the past few years has us drowning in data but often starved of knowledge. Many companies that gather huge amounts of electronic data have now begun applying data mining techniques to their data warehouses to discover and extract pieces of information useful for making smart business decisions. Effective data mining, as opposed to data dredging, requires an understanding of concepts from exploratory data analysis, pattern recognition, machine learning, heterogeneous data bases, parallel processing and data visualization, in addition to knowing the problem domain.

I will first give a series of lectures . While studying techniques for database representation/modeling, clustering, classification, finding associations and sequence processing, emphasis will be placed on the issues of algorithm scalability, performance, interpretability and ability to deal with garbage data. You will be using the Java based public domain software such as WEKA, for some class exercises. The last few classes will consist of student term-project presentations, followed by active discussion.

GRADING:
5+35+5 pts: pre-proposal presentation + Term paper (due May 1) + 20 min. presentation (groups of 2-3).
25 pts: Homework assignments
25 pts: Written Exam; Thursday, March 22, in class
5 pts: Participation in discussions.
There will be no final exam.
A set of class notes and supplementary materials will be available via HKN.

Textbook
P. Tan, M. Steinbach and V. Kumar, Introduction to Data Mining, Addison-Wesley, 2005.
Some sample chapters are available at the book's website, http://www-users.cs.umn.edu/~kumar/dmbook/index.php, but they do not cover any advanced material.

Other recommended books:
1. Hastie/Tibshirani/Friedman (2001) The Elements of Statistical Learning , Springer
Solid; stats oriented.
 2. C.M. Bishop (2006): Pattern Recognition and Machine Learning, Springer.
3. I. H. Witten and E. Frank (2nd Ed, 2005), Data Mining. Morgan Kaufmann.
Machine learning viewpoint, closely tied to the WEKA software.
From a UT computer you can access an "e-book" version http://www.netlibrary.com/AccessProduct.aspx?ProductId=130260&ReturnLabel=lnkSearchResults&ReturnPath/Search/SearchResults.aspx&PrimedSearch=witten+frank
4, J. Han and M. Kamber (2005) Data Mining: Concepts and Techniques , 2nd Ed. Morgan Kaufmann.
Database oriented.
5. Duda/Hart/Stork (2000). Pattern Classification (2nd Ed) .
Solid again. Gives pattern recognition perspective.
6. D. Hand, H. Mannila, P. Smyth (2001), Principles of Data Mining , MIT Press.
More conceptual and statistically oriented.