After successful completion of the course, students are able to:

1. Understand the basics of probabilistic (Bayesian) modeling and inference
2. Construct probabilistic models via an expressive probabilistic programming language. 
3. Understand standard inference algorithms and their implementations in probabilistic programming languages (MCMC, Variational Inference, etc.)
4. Independently read literature in the probabilistic programming space

Probabilistic programming is a general framework to express probabilistic models as programs. It lies at the intersection of machine learning, statistics, and programming languages. While it has classically been seen as mechanization of Bayesian statistical inference, it has recently emerged as a candidate for next-generation AI toolchains.

In this seminar, we will both read and discuss selected chapters from books and computational Bayesian data analysis, as well as research papers in the area of probabilistic programming. Theoretical reading will be supplemented with practical examples (i.e., written programs).

Seminar participants are expected to read the chapters or papers before attending sessions.
As a small final project, everyone (including the lecturer!) will present a probabilistic program implemented in a language of their choice (Gen, Stan, Pyro, Tensorflow Probability, etc.)

Regular discussion groups with concluding presentation of an implemented project

Mandatory attendance in the discussion rounds!

Regular participation in the discussion and presentation of the final project