MIT 6.041 Probabilistic Systems Analysis and Applied Probability

MIT's introductory probability course taught by Professor John Tsitsiklis. Lectures begin with probability models and axioms, conditioning and Bayes' rule, independence and counting, then cover discrete and continuous random variables, expectation and variance, joint distributions, derived distributions, the law of large numbers, the central limit theorem, Bayesian inference, and Markov chains. It suits engineering, CS and data science students who want solid probability before statistics or machine learning. Follow with Professor Leonard's Statistics or MIT 18.650.