| 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 prepare students for the basic skills of model-based data analysis and R language. |
| 2.Professional Knowledge in Computational Science |
| 4.Mathematical and Statistical Software Skills |
|
|
|
| Attendance |
| Oral Presentation |
| Assignment |
|
| Course Content and Homework/Schedule/Tests Schedule |
| Week |
Course Content |
| Week 1 |
Using R |
| Week 2 |
Regression tree |
| Week 3 |
Bagged trees |
| Week 4 |
Random forest |
| Week 5 |
Generalized linear model (GLM) |
| Week 6 |
Kernel basis expansion |
| Week 7 |
Suoport vector machine (SVM) |
| Week 8 |
Smoothing spline |
| Week 9 |
Additive model |
| Week 10 |
Generalized additive model (GAM) |
| Week 11 |
Tree with recursive feature elimination (RFE) |
| Week 12 |
RF with RFE |
| Week 13 |
GAM with RFE |
| Week 14 |
Artificial neural network (ANN) |
| Week 15 |
ANN |
| Week 16 |
Final report
|
self-directed
learning |
02.Viewing multimedia materials related to industry and academia.
|
|
| Evaluation |
Attendance: 50%
Report: 40%
Self-learning: 10%
The grade system is tentative and subject to modification. |
| Textbook & other References |
| The Elements of Statistical Learning by Trevor Hastie, Robert Tibshirani and Jerome Friedman |
| Teaching Aids & Teacher's Website |
|
| Office Hours |
| 1:00-2:00 PM Friday |
| Sustainable Development Goals, SDGs(Link URL) |
| 04.Quality Education | include experience courses：N |
|