Maximum Likelihood Estimation from Scratch: From Coin Flips to Gaussians

This post explains Maximum Likelihood Estimation (MLE) from first principles, starting with estimating a coin's bias and building up to Gaussian distributions. It demonstrates why log-likelihoods are used instead of raw likelihoods to avoid numerical underflow, and connects MLE to more advanced algorithms like EM.