| 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 |
