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. (Darryl, Sudipta)
Week 11. MCMC. notes (Matthew, Chunxing)
Week 12. Derandomization. notes (Sarah)
Week 13. PCP (Saurabh)
A Theoretician's Toolkit 