Date	Topic	Subject	Book	Code
1-14	Classical problems	Introduction; problems with classical methods	1.1-1.4	R
1-16	Large scale testing	Family-wise error rates		R
1-21		No class; Martin Luther King Jr. Day
1-23	Large scale testing	False discovery rates		R
1-28	Large scale testing	Local false discovery rates		R
1-30		Classes cancelled due to weather
2-04	Large scale testing	Hierarchical models and shrinkage		R
2-06	Ridge regression	Ridge regression	1.5.1-1.5.3	R
2-11	Ridge regression	Selection of λ; case studies	1.5.4-1.5.6	R
2-13	Lasso	KKT conditions, soft thresholding	2.1-2.2	R
2-18	Lasso	Algorithms	2.3-2.4	R
2-20	Lasso	Cross-validaton	2.5-2.6	R
2-25	Lasso	Case studies, Bayesian interpretation	2.7-2.9	R
2-27	Bias reduction	Adaptive lasso, MCP, and SCAD	3.1-3.2	R
3-04	Bias reduction	Algorithms; convexity	3.5-3.7	R
3-06	Bias reduction	Case studies	3.8	R
3-11	Stability	Elastic Net	4.1-4.2	R
3-13	Stability	Algorithms; case studies	4.3-4.5	R
3-18		No class; spring break
3-20		No class; spring break
3-25		ENAR
3-27		ENAR
3-29	Theory	Theoretical properties (ROOM C401)	5
4-01	Theory	Theoretical properties: Non-asymptotic	5
4-03	Inference	Marginal false discovery rates	6	R
4-08	Inference	Debiasing and subsampling/resampling	7, 9	R
4-10	Inference	Selective inference		R
4-15	Inference	Knockoff filter		R
4-17	Other likelihoods	Logistic regression	10	R
4-22	Other likelihoods	Other likelihoods	11-12	R
4-24	Structured sparsity	Group lasso	13	R
4-29	Structured sparsity	Bi-level selection	14	R
5-01	Structured sparsity	Fusion penalties	15	R
5-06	Structured sparsity	Further applications of penalization and sparsity (1:00)		R