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Probabilistic models such as logistic regression, Bayesian classification,neural networks, and models for natural language processing, are increasinglymore present in both undergraduate and graduate statistics and data sciencecurricula due to their wide range of applications. In this paper, we present aone-week course module for studnets in advanced undergraduate and appliedgraduate courses on variational inference, a popular optimization-basedapproach for approximate inference with probabilistic models. Our proposedmodule is guided by active learning principles: In addition to lecturematerials on variational inference, we provide an accompanying class activity,an \texttt{R shiny} app, and guided labs based on real data applications oflogistic regression and clustering documents using Latent Dirichlet Allocationwith \texttt{R} code. The main goal of our module is to expose students to amethod that facilitates statistical modeling and inference with large datasets.Using our proposed module as a foundation, instructors can adopt and adapt itto introduce more realistic case studies and applications in data science,Bayesian statistics, multivariate analysis, and statistical machine learningcourses.

Introducing Variational Inference in Statistics and Data Science Curriculum