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In this program

Classification and regression trees
Ensemble methods: bagging
Random forests
Boosting (AdaBoost, gradient boosting intro)
k-nearest neighbors and naive Bayes
Model evaluation: ROC and AUC
Confusion matrices

Supervised learning

k-nearest neighbors and naive Bayes

Data mining · STEM / applied

Topic

k-nearest neighbors and naive Bayes

Practice by unit: Supervised learning

Undergraduate Data mining — outline derived from course README sections and typical US statistics syllabi (OpenIntro / standard OER where applicable).

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