| 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. |
|
|
|
| Attendance |
| Oral Presentation |
| Assignment |
|
| Course Content and Homework/Schedule/Tests Schedule |
| Week |
Course Content |
| Week 1 |
Getting started with R |
| Week 2 |
More tools in R |
| Week 3 |
Basic optimization: Least square (LS) regression |
| Week 4 |
Multivariate normal distribution (MVN) and maximum likelihood (ML) regression |
| Week 5 |
GCV, AIC: Best subset regression;
|
| Week 6 |
Stepwise regression |
| Week 7 |
L2-norm regularized (ridge) regression |
| Week 8 |
Adaptive weighted ridge regression |
| Week 9 |
L1-norm regularized (LASSO) regression |
| Week 10 |
Coordinate descent method for ridge and LASSO regressions regression tree |
| Week 11 |
Network estimation (node-wise regressions) |
| Week 12 |
Bagged regression trees (random forest) |
| Week 13 |
Artificial neural network regression |
| Week 14 |
Polynomial regression and basis functions |
| Week 15 |
Ridge regression with the (spline) basis expansion |
| Week 16 |
Final report |
| Week 17 |
Self-learning (Classification problems and methods) |
| Week 18 |
Self-learning (Classification problems and methods)
|
|
| 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 |
|