Learning Machine Learning from Introduction to Statistical Learning by Trevor Hastie

3. Linear Regression

   • Key concepts: Simple Linear Regression, Multiple Linear Regression, Qualitative Predictors, one-hot encoding, F-test, Variance Inflation Factor (VIF), Collinearity, Interaction Terms, Hat Matrix, Leverage, Studentised Residual

4. Classification

   • Key concepts: Logistic Regression, Multinomial Logistic Regression, Linear Discriminant Analysis, Quadratic Discriminant Analysis, Naive Bayes, K-Nearest Neighbor, Null Classifier, ROC Curve, Confusion Matrix, Type I and Type II error

5. Resampling Methods

   • Key concepts: Cross-Validation, Bootstrap, Leave-One-Out Cross-Validation (LOOCV), K-Fold Cross-Validation

6. Linear Model Selection and Regularisation

   • Key concepts: Best subset selection, forward stepwise selection, backward stepwise selection, ridge regression, lasso, hyperparameter tuning, principal components regression, partial least squares