Outline & Objectives
| Course Outline | This course explains the background, the characteristics, and the success factors of data mining. It introduces the representative techniques of data mining such as classification, cluster analysis, shopping cart analysis, and recommendation. |
| Learning Objectives | Learning the concepts of basic mining techniques, Understanding how to design mining algorithms, Improving skills for implementing, etc. This course is not about teaching how to use data mining techniques, but rather about developing these techniques. |
Evaluation Criteria
| Mid Term | 30% |
| Final Term | 30% |
| Assignments | 20% |
| Presentation | 10% |
| Attendance | 10% |
Lecture Schedule
| Week | Lecture Topics and Contents |
|---|---|
| Week 1 | Introduction to Data Mining |
| Week 2 | Map-Reduce and the New Software Stack |
| Week 3 | Finding Similar Items Paper Presentation |
| Week 4 | Mining Data Streams Paper Presentation |
| Week 5 | Link Analysis Paper Presentation |
| Week 6 | Frequent Itemsets Paper Presentation |
| Week 7 | Frequent Itemsets Paper Presentation |
| Week 8 | Midterm exam |
| Week 9 | Clustering Paper Presentation |
| Week 10 | Advertising on the Web Paper Presentation |
| Week 11 | Recommendation Systems Paper Presentation |
| Week 12 | Recommendation Systems Paper Presentation |
| Week 13 | Mining Social-Network Graphs Paper Presentation |
| Week 14 | Mining Social-Network Graphs Paper Presentation |
| Week 15 | Project Presentation |
| Week 16 | Final Exam |