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; Stepwise regression |
第6週 |
Regularized optimization: L2-norm regularized (ridge) regression |
第7週 |
Adaptive weighted ridge regression |
第8週 |
Constrained optimization: Dual ascending method (DA), method of multipliers (MM) and Alternating direction method of multiplier (ADMM) |
第9週 |
L1-norm regularized (LASSO) regression |
第10週 |
Consensus LASSO regression and group LASSO regression |
第11週 |
Network estimation (node-wise regressions) |
第12週 |
Coordinate descent for high-dimensional ridge regression and LASSO regression |
第13週 |
Polynomial regression and basis functions |
第14週 |
Ridge regression with the (spline) basis expansion |
第15週 |
Additive model (AM) (regression with basis expansion + 0-sum condition) |
第16週 |
Final report |
第17週 |
Self-taught (Estimation of AM by the ridge regression and the weighted ridge regression) |
第18週 |
Self-taught (Sparse estimation of AM by the adaptive weighted ridge regression (AWRR))
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