Syllabus for an introductory data mining course

This syllabus assumes that the course is given twice a week, and the first week there is only one meeting. Other schedules require appropriate adjustments.
The (*) modules are more advanced and can be skipped for a more introductory level course.
Here is detailed course outline which contains the outline and study guide for each module.

Week 1: M1: Introduction: Machine Learning and Data Mining
Assignment 0: Data mining in the news (1 week)

Week 2: M2: Machine Learning and Classification
Assignment 1: Learning to use WEKA (1 week)
M3. Input: Concepts, instances, attributes

Week 3: M4. Output: Knowledge Representation
Assignment 2: Preparing the data and mining it (beginner level) (2 weeks)
M5. Classification - Basic methods

Week 4: M6: Classification: Decision Trees
M7: Classification: C4.5

Week 5: *M8: Classification: CART
Assignment 3: Data cleaning and preparation (intermediate level) (2 weeks)
*M9: Classification: more methods

Week 6: Quiz
M10: Evaluation and Credibility

Week 7: *M11: Evaluation - Lift and Costs
M12: Data Preparation for Knowledge Discovery
Assignment 4: Feature reduction (2 weeks)

Week 8: M13: Clustering
M14: Associations

Week 9: M15: Visualization
*M16: Summarization and Deviation Detection
Assignment 5, Predicting treatment outcome (1 week)

Week 10: *M17: Applications: Targeted Marketing and Customer Modeling
*M18: Applications: Genomic Microarray Data Analysis
Final Project: Predict disease classes using genetic microarray data (4 weeks)

Week 11: M19: Data Mining and Society; Future Directions
Final Exam

Weeks 12-14: Lab, work on the final project.
Project presentations are given in the last week of the term.

The modules are designed to be presented in the order given, from basic concepts to more advanced, and ending with 2 application case studies.

Data Mining Course Introduction
→ Gregory Piatetsky-Shapiro Video Intro for Students
→ Gregory Piatetsky-Shapiro Video Intro for Faculty
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