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* linear models: logistic regression
* polynomial decision rule and polynomial regression
* SVM (Support Vector Machine), kernel trick
* Overfitting: two definitions
* Model selection
* Regularization: L1, L2, elastic net.
* Decision trees
* splitting criteria for classification and regression
* overfitting in trees: prestopping and postpruning
* nonstability of trees
* feature importance
* Ensembling
* RSM, subsampling, bagging.
* Random Forest
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