Date	Topic	Subject	Book	Code
1-17	Classical problems	Introduction; problems with classical methods	1.1-1.4	R
1-19	Large scale testing	Family-wise error rates		R
1-24	Large scale testing	False discovery rates		R
1-26	Large scale testing	Local false discovery rates		R
1-31	Large scale testing	Hierarchical models and shrinkage		R
2-02	Ridge regression	Ridge regression	1.5.1-1.5.3	R
2-07	Ridge regression	Selection of λ; case studies	1.5.4-1.5.6	R
2-09	Lasso	KKT conditions, soft thresholding	2.1-2.2	R
2-14	Lasso	(cont’d)
2-16	Lasso	Algorithms	2.3-2.4	R
2-21	Lasso	Cross-validaton	2.5-2.6	R
2-23	Lasso	Case studies, Bayesian interpretation	2.7-2.9	R
2-28	Bias reduction	Adaptive lasso, MCP, and SCAD	3.1-3.2	R
3-02	Bias reduction	Algorithms; convexity	3.5-3.7	R
3-07	Bias reduction	Case studies	3.8	R
3-09	Stability	Elastic Net	4.1-4.2	R
3-14		No class; spring break
3-16		No class; spring break
3-21	Stability	Algorithms; case studies	4.3-4.5	R
3-23	Theory	Theoretical properties	5
3-28	Theory	Theoretical properties: Non-asymptotic	5
3-30	Inference	Marginal false discovery rates	6	R
4-04	Inference	Debiasing and subsampling/resampling	7, 9	R
4-06	Inference	Selective inference		R
4-11	Inference	(cont’d)
4-13	Inference	Knockoff filter		R
4-18	Other likelihoods	Logistic regression	10	R
4-20	Other likelihoods	Other likelihoods	11-12	R
4-25	Structured sparsity	Group lasso	13	R
4-27	Structured sparsity	Bi-level selection	14	R
5-02	Structured sparsity	Fusion penalties	15	R
5-04	Structured sparsity	Further applications of penalization and sparsity		R
5-08	Final projects	Penalized GEEs (paper)
		Spatiotemporal exposure prediction (paper)
		Spectral deconfounding (paper)
		Preconditioning for sign consistency (paper)
		ADMM for quantile regression (paper)
		Best subset selection (paper 1) (paper 2)