Skip to content
Free public beta until December 4, 2026: every course is open, no card needed. After that, the first chapter of each course stays free and full access is a one-time $39 ($19 for up to 100 founding members chosen for helpful feedback). Your progress is kept. How the beta works

probability

Conditional probability

taught

Taught in

Requires

Required by

Local knowledge map

Local knowledge map

Base rates and the prosecutor's fallacy Bayes' theorem in odds form Independence of random variables Naive Bayes classification Pairwise versus mutual independence Prior, likelihood, and posterior Random walks The gambler's fallacy The law of total probability Tree diagrams for sequential experiments Bayes' theorem Conditional distributions Gambler's ruin Independence The Monty Hall problem The multiplication rule Conditional probability Conditional expectation Expectation for a continuous variable Expected value of a function of X (LOTUS) Expected value: the long-run average interpretation Games of chance and expected winnings Linearity of expectation Marginal distributions Probability vocabulary for ML: Bayes' theorem,… Surprise and entropy The Bernoulli distribution The Poisson distribution The discrete uniform distribution Variance, defined Expected value Counting permutations combinations

Click to expand

Solid arrows are required prerequisites; dashed are recommended or helpful. Lower rows are more foundational. Click any box to jump to that concept.