From random variables and the probability mass function through expectation and variance to the Bernoulli, binomial and Poisson distributions, and how changing word probabilities lets an AI text carry a watermark.
From probability symbols and how to read them through Kolmogorov's three axioms, the complement and OR rules and conditional probability to independence: when two probabilities can simply be multiplied, and when conditioning changes that.
A free Stanford course on the probability behind modern AI: who teaches it, the one-teacher-per-ten-students model, the weekly section, and the six-week plan.