Date	Topic	Subject	Book	Code
1-25	Classical problems	Introduction; problems with classical methods	1.1-1.4	R
1-27	Large scale testing	Family-wise error rates		R
2-01	Large scale testing	False discovery rates		R
2-03	Large scale testing	Local false discovery rates		R
2-08	Large scale testing	Hierarchical models and shrinkage		R
2-10	Ridge regression	Ridge regression	1.5.1-1.5.3	R
2-15	Ridge regression	Selection of λ; case studies	1.5.4-1.5.6	R
2-17	Lasso	KKT conditions, soft thresholding	2.1-2.2	R
2-22	Lasso	Algorithms	2.3-2.4	R
2-24	Lasso	Cross-validaton	2.5-2.6	R
3-01	Lasso	Case studies, Bayesian interpretation	2.7-2.9	R
3-03	Bias reduction	Adaptive lasso, MCP, and SCAD	3.1-3.2	R
3-08	Bias reduction	Algorithms; convexity	3.5-3.7	R
3-10	Bias reduction	Case studies	3.8	R
3-15	Stability	Elastic Net	4.1-4.2	R
3-17	Stability	Algorithms; case studies	4.3-4.5	R
3-22	Theory	Theoretical properties	5
3-24	Theory	Theoretical properties: Non-asymptotic	5
3-29	Inference	Marginal false discovery rates	6	R
3-31	Inference	Debiasing and subsampling/resampling	7, 9	R
4-05	Inference	Selective inference		R
4-07	Inference	Knockoff filter		R
4-12	Other likelihoods	Logistic regression	10	R
4-14		Instructional break, no class
4-19	Other likelihoods	Logistic regression (cont’d)	10	R
4-21	Other likelihoods	Other likelihoods	11-12	R
4-26	Structured sparsity	Group lasso	13	R
4-28	Structured sparsity	Bi-level selection	14	R
5-03	Structured sparsity	Fusion penalties	15	R
5-05	Structured sparsity	Further applications of penalization and sparsity		R