MIT 6.041 Probabilistic Systems Analysis and Applied Probability is a probability course taught at MIT by Prof. John Tsitsiklis. Lecture videos, slides, problem sets and exams are free on MIT OpenCourseWare.
What’s in it? Probability models, conditional probability and Bayes’ rule, discrete and continuous random variables, covariance, Bernoulli and Poisson processes, Markov chains, the law of large numbers, the central limit theorem, and Bayesian and classical statistical inference.
Why we recommend it: EEG is a noisy signal, participants differ from one another and every classification result is a probability. Deciding whether a result is meaningful, understanding why a model got something wrong or seeing the assumptions behind machine learning methods all require a solid foundation in probability. Tsitsiklis teaches it in an orderly and exceptionally clear way.
Who is it for? Anyone who knows calculus and wants a solid introduction to probability.
- Course page: MIT OCW, 6.041