API 222 Section Materials

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.

Section materials

section-01

Intro to API 222 and R ·vectors, matrices, data frames, basic operations  view

section-02

KNN and Linear Regression ·k-nearest neighbors, predictive modeling fundamentals  view

section-03

Linear Regression Exercises ·inference, model fitting, interpretation  view

section-04

Classification ·logistic regression, LDA, performance metrics  view

section-05

Cross-Validation, Ridge, Lasso, and Bootstrapping ·resampling, regularization  view

section-06

Regularization and Dimension Reduction ·PCA, PCR, advanced regularization  view

section-07

Non-linear Models ·polynomial regression, splines, local regression  view

section-08

Tree-Based Methods ·decision trees, bagging, random forests, boosting  view

section-09

Support Vector Machines ·SVMs, classifiers, kernel approaches  view

section-10

Neural Networks and Deep Learning ·deep learning architectures, reinforcement learning  view