Fundamentals of Probability
A graduate-level treatment of probability theory from MIT OpenCourseWare, covering sample space, random variables, expectations, and transforms before moving into Bernoulli and Poisson processes, finite Markov chains, and limit theorems. Additional topics include measure-theoretic language and terminology, interchange of limits and expectations, multivariate Gaussian distributions, and conditional distributions and expectations treated with real rigor. The course is built for first or second-year graduate students who already have a mathematical background and want the formal foundations rather than an applied introduction. Materials come from MIT's OpenCourseWare archive, drawing on the Electrical Engineering and Computer Science department alongside Sloan School coursework, and typically include lecture notes, problem sets, and exams. There is no instructor interaction or certificate, just the full set of course materials free to work through at your own pace.