API 222 Section Materials
Section materials for API-222 covering statistical learning, machine learning methods, and their applications in policy analysis. Materials build on contributions from previous TFs including Ibou Dieye, Laura Morris, Emily Mower, and Amy Wickett.
Intro to API 222 and R ·vectors, matrices, data frames, basic operations view
KNN and Linear Regression ·k-nearest neighbors, predictive modeling fundamentals view
Linear Regression Exercises ·inference, model fitting, interpretation view
Classification ·logistic regression, LDA, performance metrics view
Cross-Validation, Ridge, Lasso, and Bootstrapping ·resampling, regularization view
Regularization and Dimension Reduction ·PCA, PCR, advanced regularization view
Non-linear Models ·polynomial regression, splines, local regression view
Tree-Based Methods ·decision trees, bagging, random forests, boosting view
Support Vector Machines ·SVMs, classifiers, kernel approaches view
Neural Networks and Deep Learning ·deep learning architectures, reinforcement learning view