bayesian

Build the vocabulary that underlies all of ML: sample spaces, discrete and continuous random variables, PMFs, PDFs, and CDFs. Then tour the key distributions — Bernoulli, Binomial, Categorical, Gaussian, Poisson, Exponential, Uniform — with their parameters, mean, variance, and exactly when each appears in practice.

Understand what it really means to update beliefs with data. Derive Bayes' theorem from first principles, dissect the roles of prior, likelihood, posterior, and evidence, work through a complete Beta-Binomial conjugate example numerically, and see why the base-rate fallacy trips up even experts.