Probability & Statistics

Foundational probability theory and statistical inference. Answers the question: how do we reason about uncertainty and draw conclusions from data?

Notes

• Probability Theory — axioms, conditional probability, Bayes’ theorem, random variables, expectation
• Probability Distributions — Bernoulli, Binomial, Poisson, Gaussian, Beta, Dirichlet, exponential family
• Bayesian Inference — prior, likelihood, posterior, MLE vs MAP, conjugate priors, approximate inference
• Logistic Regression — binary classification, sigmoid, cross-entropy, MLE connection

Links

← 02 — Calculus & Analysis → 04 — Optimization

• 05 — Statistical Learning Theory
• Probabilistic Models