Date	Topic	Subject	Book	Code
1-20	Classical problems	Introduction; problems with classical methods	1.1-1.4	R
1-25	Large scale testing	Family-wise error rates		R
1-27	Large scale testing	False discovery rates		R
2-1	Large scale testing	Local false discovery rates		R
2-3	Large scale testing	Hierarchical models and shrinkage		R
2-8	Ridge regression	Ridge regression	1.5.1-1.5.3	R
2-10	Ridge regression	Selection of λ; case studies	1.5.4-1.5.6	R
2-15	Lasso	KKT conditions, soft thresholding	2.1-2.2	R
2-17	Lasso	Algorithms	2.3-2.4	R
2-22	Lasso	Cross-validaton	2.5-2.6	R
2-24	Lasso	Case studies, Bayesian interpretation	2.7-2.9	R
2-29	Bias reduction	Adaptive lasso, MCP, and SCAD	3.1-3.2	R
3-2	Bias reduction	Theoretical results for MCP, SCAD, and lasso
3-07		ENAR
3-09		ENAR
3-14		Spring break
3-16		Spring break
3-21	Bias reduction	Algorithms; convexity	3.5-3.6	R [sim1] [sim2]
3-23	Bias reduction	Case studies	3.7	R
3-28	Stability	Elastic Net	4.1, 4.2	R
3-30	Stability	Algorithms; case studies	4.3-4.5	R
4-4	Inference	Semi-Penalized Inference with Direct FDR Control	6.2, 6.6, 7 [paper]
4-6	Structured sparsity	Treatment effects and concave fusion	[paper]
4-11	Inference	False inclusion rates	5	R
4-13	Inference	Sample splitting and resampling	8	R
4-18	Inference	Selective inference		R
4-20	Other likelihoods	Logistic regression	9	R
4-25	Other likelihoods	Other likelihoods	10, 12	R
4-27	Structured sparsity	Group lasso	13
5-2	Structured sparsity	Group lasso (cont'd)
5-4	Structured sparsity	Bi-level selection
5-11	Structured sparsity	Further applications of penalization and sparsity