Introduction to Probability
Probability theory and statistical inference form the core of this MIT OpenCourseWare course, a companion to 6.041SC Probabilistic Systems Analysis and Applied Probability. Built from video lectures developed for the edX version of the class, it covers discrete and continuous random variables, expectation, conditional probability, Bayes rule, limit theorems, and an introduction to statistical inference. The materials include problem sets with solutions and exam practice, letting students work through the same content MIT undergraduates use in its electrical engineering and computer science curriculum. Topics build from basic counting and probability axioms toward more advanced tools like the law of large numbers and the central limit theorem, giving a foundation for later work in statistics, machine learning, and data analysis. All course materials are free to access under MIT's open license.