Introduction
Two variables are independent if knowing one tells you nothing about the other. Independence makes joint distributions factorise into simple pieces, which is what makes most ML models computable. The assumption that training examples are independent and identically distributed (i.i.d.) underlies almost every learning algorithm and every train/test split.