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machine-learning

Probability vocabulary for ML: Bayes' theorem, distributions, expectation

taught

The Bayes'-theorem, distribution, and expectation vocabulary that generative classifiers and Bayesian estimation both lean on.

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Local knowledge map

From logistic regression to a neural network Multiclass logistic regression Naive Bayes, revisited for classification Probability calibration Binary logistic regression Gaussian mixture models and the EM algorithm Gaussian processes Generative classifiers: LDA and QDA Probability vocabulary for ML: Bayes' theorem,… Base rates and the prosecutor's fallacy Bayes' theorem in odds form Choosing a continuous model Conditional expectation Expected value of a function of X (LOTUS) Expected value: the long-run average interpretation Gambler's ruin Games of chance and expected winnings K-means clustering Linearity of expectation Naive Bayes classification Normal approximation to the binomial Prior, likelihood, and posterior Simple linear regression and least squares Surprise and entropy The Bernoulli distribution The Monty Hall problem The Poisson distribution The central limit theorem The discrete uniform distribution The kernel trick and support vector machines The standard normal distribution Variance, defined Bayes' theorem Expected value The normal distribution Conditional probability Counting permutations combinations Expectation for a continuous variable

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