mathfoundationsstatistics
Probability theory
Random variables, distributions, expectation and Bayesian reasoning — core language of uncertainty in ML.
Уровни глубины
L0Intro~3ч
Understands events, probability rules, conditional probability, Bayes theorem at a conceptual level.
L1Basics~15ч
Works with discrete and continuous distributions (Bernoulli, Gaussian, Poisson); computes expectations, variances, covariance.
L2Working~25ч
Derives MLE/MAP; uses CLT, moment generating functions; comfortable with multivariate Gaussians.
L3Advanced~40ч
Designs probabilistic graphical models; derives variational inference bounds; analyses martingales.
L4Research~80ч
Contributes to Bayesian deep learning, causal inference, or information-theoretic ML.
Ресурсы
L0 — Intro
L1 — Basics