| 週次 |
授課內容 |
| 第1週 |
Polynomial regression |
| 第2週 |
Basis functions and data transformation |
| 第3週 |
Ridge regression with the basis expansion |
| 第4週 |
Additive model (AM) (I) (regression with basis expansion + 0-sum condition) |
| 第5週 |
Estimation of AM by the ridge regression and the weighted ridge regression |
| 第6週 |
Sparse estimation of AM by the adaptive weighted ridge regression (AWRR) |
| 第7週 |
Spring break |
| 第8週 |
Sparse estimation of AM by the group LASSO regression |
| 第9週 |
Smoothing splines |
| 第10週 |
Scatter plot smoothing |
| 第11週 |
Backfittinig algorithm |
| 第12週 |
Kernel functions |
| 第13週 |
Regression with kernel basis expansion |
| 第14週 |
Regularized estimation of regression with kernel basis expansion |
| 第15週 |
Sparse estimation of kernel basis expansion |
| 第16週 |
Final report |
| 第17週 |
Self-taught: Sparse estimation of partial correlation network (node-wise regression approach) |
| 第18週 |
Self-taught: Sparse estimation of Gaussian graphical model (GGM) |