Adapted (mostly verbatim) from Prof Larry Wasserman's excellent book "All of Statistics".

	Statistics		Computer Science		Meaning
	estimation		learning		using data to estimate an unknown quantity
	classification		supervised learning		predicting discrete Y from X (groups/categories are known)
	clustering		unsupervised learning		putting data into "clusters" (categories are not known before hand)
	data		training sample		$(X$1, Y1), (X2, Y2), (X3, Y3), ... , (Xn-1, Yn-1), (Xn, Yn)
	covariates		features		the Xi's
	classifier		hypothesis		a map from covariates to outcomes
	hypothesis		---		subset of a parameter space $\Theta$
	confidence interval		---		interval that contains an unknown quantity with given frequency
	directed acyclic graph		Bayes net		multivariate distribution with given conditional independence relations
	Bayesian inference		Bayesian inference		statistical methods for using data to update beliefs
	frequentist inference		---		statistical methods with guaranteed frequency behavior
	large deviation bounds		PAC learning		uniform bounds on probability of errors