Intro to Machine Learning

Presenter: Grant Gordon

Install R Packages:

• nnet, randomForest, e1071

Selected Background Papers:

• Murphy, Kevin P. 2012. Machine Learning: A Probabilistic Perspective. Cambridge, MA: The MIT Press.
  • Read Chapter 1: Introduction
• Brieman, Leo. 2001. “Statistical Modeling: The Two Cultures.” Statistical Science. 16(3): 199-231.
• Hastie, Trevor and Robert Tibshirani and Jerome Friedman. 2008. “The Elements of Statistical Learning: Data Mining, Inference and Prediction.” New York, NY: Springer Series.
  • Read Chapter 7: Model Assessment and Selection
  • Read this
  • Note: When you have time, read the rest of this book!

Applied Papers

Note: These papers feature two applications of machine learning to political science.

1. Predicting Conflict
   • Beck, Nathaniel, Gary King and Langsche Zeng. 2000. I”mproving Quantitative Studies of International Conflict: A conjecture.” The American Political Science Review. 94(1): 21-35.
   • Ward, Michael D and Brian Greenhill and Kristin Bakke. 2010. “The Perils of Policy by P-value: Predicting Civil Conflicts” Journal of Peace Research. 47(4):363-375.
2. Heterogeneous Treatment Effects
   • Imai, Kosuke and Marc Ratkovic. 2013. “Estimating Treatment Effect Heterogeneity in Randomized Program Evaluation.” The Annals of Applied Statistics. 7(1): 443-470.
   • Green, Donald P. and Holger L. Kern. 2012. “Modeling Heterogeneous Treatment Effects in Survey Experiments with Bayesian Additive Regression Trees.” Public Opinion Quarterly. 76: 491-511

Other Resources.

Note: Many of the leading professors in machine learning post their course lectures, problem sets (and answers, and code online). A few worth visiting are:

• Andew Ng, Stanford
• Christopher Volinsky, Columbia
• Alex Smola, CMU