Use MENACE and tic-tac-toe to move from one-step bandit feedback to state-dependent policies and delayed rewards.

Move from fixed A/B testing to online learning with Bayesian multi-armed bandits, probability matching, and Thompson sampling.

Connect fixed A/B testing to Bayesian posterior uncertainty over click-through rates, and use that uncertainty to make one final decision.

See how Beta-Bernoulli updates work one observation at a time in A/B tests and bandits, and how the Pólya urn explains self-reinforcing feedback.

Understand the Beta distribution as a model for unknown success probabilities, and derive the Beta-Bernoulli update for binary data.