CMU 07-280 Lecture 6: How Decision Trees Split Data with Mutual Information
Lecture 6 recursively grows a tree from decision stumps, measures label uncertainty with entropy, and selects splits by `I(Y;W)=H(Y)-H(Y|W)`; this is computationally practical greedy ERM, not a global optimal-tree guarantee.