Topics

Course outline (updated April 23): toolkit.pdf

Week 1. Probabilistic Inequalities. notes (Weijun)

Week 2. Random Graphs. notes (Kevin)

Week 3. SVD and Tensor Decomposition. notes (Shanshan, Ching-An)

Week 4. Matrix approximation via Sampling and Subspace Embedding. notes (Rakshith, Peng)

Week 5. Convex Optimization. notes (Zihao, Xin)

Week 6. Error-correcting Codes.  notes (Yan)

Week 7. Lattices, basis reduction. notes (Digvijay)

Week 8. PAC learning, VC dimension. notes (Ezgi, Kyle)

Week 9. Boolean functions. notes (Emma)

Week 10. High-dimensional Geometry. notes (Darryl, Sudipta)

Week 11. MCMC. notes (Matthew, Chunxing)

Week 12. Derandomization. notes (Sarah)

Week 13. PCP (Saurabh)