API 222 Section Materials
API 222 Section Materials
Comprehensive section materials for API-222 covering statistical learning, machine learning methods, and their applications in policy analysis.
Section Materials
Intro to API 222 and R
Introduction to the course and R programming fundamentals including vectors, matrices, data frames, and basic operations.
KNN and Linear Regression
K-Nearest Neighbors algorithm and linear regression fundamentals for predictive modeling.
Linear Regression Exercises
Hands-on exercises for practicing inference, model fitting, and interpretation in linear regression.
Classification
Classification methods including logistic regression, linear discriminant analysis, and performance metrics.
Cross-Validation, Ridge, Lasso, and Bootstrapping
Resampling methods for model evaluation and regularization techniques for improved prediction accuracy.
Regularization and Dimension Reduction
Advanced regularization methods and dimension reduction techniques including PCA and PCR.
Non-linear Models
Moving beyond linearity with polynomial regression, splines, and local regression methods.
Tree-Based Methods
Decision trees, bagging, random forests, and boosting for classification and regression problems.
Support Vector Machines
Support vector classifiers and support vector machines for classification with various kernel approaches.
Neural Networks and Deep Learning
Introduction to neural networks, deep learning architectures, and reinforcement learning concepts.
About These Materials
This page contains all of the code and notes for API-222 section. The materials build on contributions from previous teaching fellows including Ibou Dieye, Laura Morris, Emily Mower, and Amy Wickett. If you have any questions or need help with anything, please don’t hesitate to reach out.