This course trains students to use statistical models for forecasting societal, mainly political, outcomes The students learn how to use Machine Learning and Data Mining algorithms to explore topics such as measuring the extent of partisan polarization, predicting electoral outcomes, predicting local violence, analyzing the trend of interstate war, and forecasting civil war. Subjects to be covered include understanding the differences and similarities between Correlation Analysis, Causal Inference, and Forecasting Principles; Naive Bayes; k-Nearest Neighbors (KNN); Regularized Linear Regression (Lasso, Ridge, eNet); forecasting using Maximum Likelihood Estimation (MLE); Trees methods; Clustering; and Dimension Reduction.