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ai deep-dive Reading CMU 07-280

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.

#cmu#ai-course#decision-trees#information-theory#machine-learning
learning guide Reading CMU 10-301 Machine Learning

CMU 10-301 HW2: From Information Calculations to a Complete Decision Tree

HW2 moves from hand-calculated entropy and mutual information to an end-to-end tree learner, predictor, and evaluator.

#cmu#machine-learning#decision-tree#information-theory