Lecture 1, Sept 11:  Introduction, random models, the giant component

Lecture 2, Sept 18:  first moment upper bounds, the algorithmic gap, concentration, branching process

Lecture 3, Sept 25: second moment method: k-SAT, NAE-SAT, k-COL

Lecture 4, Oct 2:   sharp thresholds

Lecture 5, Oct 9:  analysis of greedy algorithms

Lecture 6, Oct 16: random graphs on a given degree sequence, k-cores

Lecture 7, Oct 23:  clustering in XOR-SAT, frozen variables

Lecture 8, Oct 30: clustering and freezing in k-SAT

Lecture 9, Nov 6: condensation, planted model, asymptotic thresholds for clustering and freezing

Lecture 10, Nov 13:  The Survey Propogation algorithm

Lecture 11, Nov 20: The freezing threshold for k-COL; the asymptotic NAE-SAT threshold

Lecture 12, Nov 27:  Methods from statistical physics, including how to derive fixed point equations for thresholds (Dmitry Panchenko), the asymptotic k-SAT threshold, brief comments on the exact k-SAT threshold.