| 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.
|
| 3.Professional Knowledge in Statistical Analysis |
|
|
|
| Written Presentation |
| Assignment |
| Attendance |
|
| Course Content and Homework/Schedule/Tests Schedule |
| Week |
Course Content |
| Week 1 |
Getting started with R
|
| Week 2 |
More tools in R |
| Week 3 |
Multivariate normal distribution |
| Week 4 |
Linear regression I |
| Week 5 |
Linear regression II (national holiday) |
| Week 6 |
GCV, AIC and best subset regression |
| Week 7 |
Stepwise regression (national holiday) |
| Week 8 |
L2-norm regularized (ridge) regression |
| Week 9 |
Adaptive weighted ridge regression |
| Week 10 |
Nonlinear regression: Regression tree |
| Week 11 |
Bootstrap aggregation and random forest regression |
| Week 12 |
K-fold cross-validation (CV) |
| Week 13 |
Boosted trees |
| Week 14 |
Implementing regressions with caret |
| Week 15 |
Final report |
| Week 16 |
Final report |
self-directed
learning |
03.Preparing presentations or reports related to industry and academia.
|
|
| Evaluation |
Homework assignments and class attendance watching lecture videos: 45%
Report: 45%
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 |
|