A course on multilevel models, including the hierarchical linear model, models for cross-classified and multiple membership data, and models for categorical and limited dependent variables.
Deep Learning for Text and Vision
A course taught in Python introducing various deep learning methods for text and visual data.
Fundamentals of Electoral Research
This course combines theories of electoral behavior with choice models that allow social scientists to understand how voters decide.
An introduction to data science using R. This course covers the logic of predictive modeling, as well as kNN, CART, random forests, probabilistic learners, XGBoost, SVMs, and linear models.
Research Design for Causal Inference
A course on causal inference that links different philosophical perspectives on causation with different methodologies.
An introduction into social network analysis with an emphasis on modeling and simulation.
This course is a combined introduction to maximum likelihood as well as Bayesian inference geared toward social scientists.
Mathematics for Social Scientists
Over the course of my career, I have had to teach many mathematical concepts. Here you find some of my teaching materials.