These notes cover the probability foundations essential to understanding machine learning models:
- Random Variables & Events: Sample spaces, probability axioms, conditional probability, and independence.
- Bayes’ Theorem: Prior, likelihood, posterior — the engine behind Bayesian inference and Naive Bayes classifiers.
- Distributions: Bernoulli, Binomial, Gaussian, Poisson — properties, PDFs, CDFs, and when each arises in ML.
- Expectation & Variance: Moments, law of large numbers, and their role in loss functions and estimators.