**Office Hours (3433 A.V. Williams Bldg): **M,W 4-5:15 or by appointment

**Course Objectives:** The course
will cover core topics in machine learning and their applications to data mining
and knowledge discovery. Topics include: statistical learning models; decision
trees; rule-based and nearest neighbor classifiers; neural networks; Bayesian
networks; support vector machines; clustering; and ensemble methods.

**Textbook:** Introduction to Data
Mining, Tan, Steinback and Kumar, Pearson, Addison-Wesley, 2006.

**Exams: Midterms (October 7 and November 18) Paper (due
December 15) **

**Preliminary References for Web Data Mining:**

- Web Mining and Web Usage Analysis: http://webmining.spd.louisville.edu/webkdd08/
- Mining the Web, Soumen Chakrabarti, Morgan-Kaufman, 2002: http://www.cse.iitb.ac.in/~soumen/mining-the-web/
- Web Data Mining, Bing Liu, Springer, 2006: http://www.cs.uic.edu/~liub/WebMiningBook.html
- Web Mining Research: A Survey, R. Kosala and H. Blockeel, 2000.

**Assignments:**

**Homework #1: Due Wednesday, September 16 - **Problems 13,
14, 15, 18, 24 from Chapter 2. **Solutions**

**Homework#2: Due Wednesday, September 23 **- Problems 3,
5, 6 from Chapter 4. **Solutions**

**Homework#3: Due Wednesday, September 30 **- Problems 7,
9, 10 from Chapter 4 **Solutions**

**Homework#4: Due Wednesday, October 7 **- Problems 4, 5
from Chapter 5 **Solutions**

**Homework#7: Due Wednesday, November 4 - **Problems 7, 8,
10, 12 from Chapter 5 **Solutions**