Introducing Variational Inference in Undergraduate Statistics and Data Science Curriculum

Probabilistic models such as logistic regression, Bayesian classification, neural networks, and models for natural language processing, are increasingly more present in the undergraduate statistics and data science curriculum due to their wide range of applications. In this paper, we present a one-week undergraduate course module on variational inference, a popular optimization-based approach for approximate inference with probabilistic models. Our proposed module is guided by active learning principles: In addition to lecture materials on variational inference, we provide an accompanying class activity, an \texttt{R shiny} app, and a guided lab based on a real data application of clustering documents using Latent Dirichlet Allocation with \texttt{R} code. The main goal of our module is to expose undergraduate students to a method that facilitates statistical modeling and inference with large datasets. Using our proposed module as a foundation, instructors can adopt it and adapt to introduce more realistic use cases and applications in data science, Bayesian statistics, multivariate analysis, and statistical machine learning courses.