- Introduction
- Data Preprocessing
- Exploring Data
- Classication: Basic Concepts, Decision Trees, and Model Evaluation
- Classication: Alternative Techniques
- Association Analysis: Basic Concepts and Algorithms
- Association Analysis: Advanced Concepts
- Cluster Analysis: Basic Concepts and Algorithms
- Cluster Analysis: Additional Issues and Algorithms
- Week 1 to 8: Instructor Teaching
- Week 9: Mid-term Exam
- Week 10 to 12: Reading Assignment
- Week 13 to 15: Case Study
- Week 16 to 18: Project presentation
- Pang-Ning Tan, Michael Steinbach, and Vipin Kumar, Introduction to Data Mining, Pearson International Edition, 2005. Slides: http://www-users.cs.umn.edu/~kumar/dmbook/index.php
- Data Mining: Concepts and Techniques, J. Han and M. Kamber, Morgan Kaufmann , 2000. Slides: http://www.cs.sfu.ca/~han/DM_Book.html
- Ethem Alpaydin, Introduction to Machine Learning, MIT Press, 2004. Slides: http://www.cs.umd.edu/class/spring2004/cmsc726/courseTopicsPage.html
Grading Policy
- Homework (25%)
- Mid-term (25%)
- Paper presentation (20%)
- Final project (20%)
- Course involvement (10%)
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