| Relevance of Course Objectives and Core Learning Outcomes(%) |
Teaching and Assessment Methods for Course Objectives |
| Course Objectives |
Competency Indicators |
Ratio(%) |
Teaching Methods |
Assessment Methods |
| To learn the techniques for extracting interesting patterns from the large databases. |
|
|
|
|
| Course Content and Homework/Schedule/Tests Schedule |
| Week |
Course Content |
| Week 1 |
The topics to be covered are as follows:
1. Introduction
2. Data
3. Exploring Data
4. Classification: Basic Concepts, Decision Trees, and Model Evaluation
5. Classification: Alternative Techniques
6. Association Analysis: Basic Concepts and Algorithms
7. Association Analysis: Advanced Concepts
8. Cluster Analysis: Basic Concepts and Algorithms
9. Cluster Analysis: Additional Issues and Algorithms
10. Anomaly Detection
11. SAS/Enterprise Miner, WEKA |
| Week 2 |
|
| Week 3 |
|
| Week 4 |
|
| Week 5 |
|
| Week 6 |
|
| Week 7 |
|
| Week 8 |
|
| Week 9 |
|
| Week 10 |
|
| Week 11 |
|
| Week 12 |
|
| Week 13 |
|
| Week 14 |
|
| Week 15 |
|
| Week 16 |
|
| Week 17 |
|
| Week 18 |
|
|
| Evaluation |
|
| Textbook & other References |
|
| Teaching Aids & Teacher's Website |
|
| Office Hours |
|
| Sustainable Development Goals, SDGs |
| include experience courses：N |
|