Course No.：21626Z
Period：10
Credits：0.5
Course Category：Lecture
Primary Coverage：
1.l1 regularization and its variants (1 lecture)
2.High dimensional graphical models and re-sampling methods (2 lectures)
Gaussian graphical models
Model selection/tuning via re-sampling
Applications: sparse regression models for building genetic regulatory networks
3.Large scale simultaneous testing (2 lectures)
Multiple hypothesis testing framework and control of type I error rates: family-wise error rate (fwer) procedures, false discovery rate (fdr) procedures, local fdr, etc
Estimating the null distribution and the proportion of null effects: empirical Bayes approach, generalized Fourier approach
Author：Jie Pang
