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Materials from my Princeton University course WWS586A: Machine Learning for Policy Analysis
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WWS586A-Machine-Learning-Policy-Analysis

Materials from my Princeton University course WWS586A: Machine Learning for Policy Analysis

Topics covered

  • Policy analysis in the digital age

  • Programming in R, R Notebooks & Github

  • Data acquisition and cleaning: APIs & webscraping

  • Natural language processing: tokenization, text processing, document-term matrix, TF-IDF weighting.

  • Introduction to statistical learning theory: bias-variance tradeoff, training, testing, cross-validation.

  • Model selection: regularization, LASSO, Bayesian LASSO.

  • Supervised learning: kNN, Naive Bayes, Decision Trees, SVMs, Neural Networks.

  • Unsupervised learning: k-means clustering, latent dirichlet allocation (topic models).

  • Special topics: machine learning and causal inference: Bayesian structural time series.

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