Probabilistic Systems Analysis and Applied Probability
MIT's foundational course on probability theory and statistical inference, taught by Professor John Tsitsiklis. The curriculum covers modeling and analyzing uncertainty, including random variables, distributions, Bayesian inference, limit theorems, Markov chains, and their applications across engineering and science. Materials include full lecture videos, lecture slides and readings, recitation problems with solutions, recitation help videos from MIT teaching assistants, tutorial problems and solutions, and complete problem sets and exams with solutions. A complementary set of EdX videos covering similar material in more depth is also referenced. Offered through MIT OpenCourseWare, the course is designed for independent self-study, giving learners everything needed to work through the material without an instructor. No certificate is offered, but the depth of practice materials makes it suitable as a rigorous self-taught introduction to applied probability.