| 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 understand the statistical learning methods and acquire the skills of data analysis using R. |
| 1.Mathematical Thinking and Logic |
| 7.Literature Review |
|
|
|
| Oral Presentation |
| Attendance |
| Assignment |
| Written Presentation |
|
| Course Content and Homework/Schedule/Tests Schedule |
| Week |
Course Content |
| Week 1 |
Using R language |
| Week 2 |
Using R language |
| Week 3 |
Multivariate normal distribution |
| Week 4 |
Least square (LS) regression |
| Week 5 |
Maximum likelihood (ML) regression |
| Week 6 |
GCV, AIC: Best subset regression |
| Week 7 |
Stepwise regression |
| Week 8 |
L2-norm regularized (ridge) regression (RR) |
| Week 9 |
Adaptive weighted ridge regression (AWRR) |
| Week 10 |
Polynomial regression; Basis function and data transformation |
| Week 11 |
Ridge regression with the (spline) basis expansion |
| Week 12 |
Additive model (I) with the RR |
| Week 13 |
Additive model (II) with the AWRR |
| Week 14 |
Smoothing spline (I) |
| Week 15 |
Smoothing spline (II) |
| Week 16 |
Final reports |
self-directed
learning |
03.Preparing presentations or reports related to industry and academia.
|
|
| Evaluation |
Class attendance, class performance, quiz: 45%
Final report: 45%
Self-learning: 10%
The grade system is tentative and subject to modification.
|
| Textbook & other References |
The elements of statistical learning by Hastie et al.
Distributed optimization and statistical learning via the alternating direction method of multipliers by Boyd et al.
|
| Teaching Aids & Teacher's Website |
Supplementary web resources
|
| Office Hours |
2:00-3:00 PM Friday
|
| Sustainable Development Goals, SDGs(Link URL) |
| 04.Quality Education | include experience courses：N |
|