Special Seminar in Applied Probability and Stochastic Processes
A doctoral-level seminar from MIT covering current research topics in applied probability. Topics for this semester span statistical physics (local weak convergence and correlation decay), artificial intelligence (belief propagation algorithms), theoretical computer science (random K-SAT, graph coloring, average-case complexity), and electrical engineering (low density parity check codes). Materials are provided through MIT OpenCourseWare and typically include a syllabus, readings, and problem sets tied to each topic area. The seminar is aimed at advanced graduate students already familiar with probability theory who want exposure to how stochastic methods cross into physics, AI, and coding theory. No certificate is offered, and the course is free to access as part of MIT's open publishing of course content under a Creative Commons license.