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