Calendar

SES #	TOPICS	KEY DATES
1	Overview and Two Open Problems
2-4	Principal Component Analysis in High Dimensions and the Spike Model
5-7	Graphs, Diffusion Maps, and Semi-supervised Learning
8-11	Spectral Clustering and Cheeger’s Inequality	Problem Set 1 due
12-14	Concentration Inequalities, Scalar and Matrix Versions	Problem Set 2 due
15-16	Johnson-Lindenstrauss Lemma and Gordon’s Theorem	Problem Set 3 due
17	Local Convergence of Graphs and Enumeration of Spanning Trees
18-19	Compressed Sensing and Sparse Recovery	Project Abstract due
20	Group Testing and Error-Correcting Codes	Problem Set 4 due
21	Approximation Algorithms and Max-Cut
22	Community Detection and the Stochastic Block Model	Problem Set 5 due
23	Synchronization Problems and Alignment
24	Project Presentations	Project Presentations
25	Project Report	Project Report due

Course Info