This repository contains materials for a full-day workshop on Probabilistic Machine Learning, with a focus on Bayesian modeling workflows and deep probabilistic models using Pyro and PyMC.
workshop.md– Description of the conference-style Probabilistic Machine Learning in Practice workshop, including schedule, learning objectives, and software setup.syllabus_NEU.md– A graduate course syllabus from Northeastern University on applied probabilistic modeling, which provides additional context and an extended curriculum around similar topics.
You can use this repository to:
- Prepare or adapt the workshop for conferences, symposia, or internal trainings.
- Draw on the graduate course syllabus as a template or reference for a semester-long offering.
- Reuse or extend the structure and themes (Bayesian workflows, hierarchical models, deep probabilistic modeling, etc.) in your own teaching or self-study.
All materials in this repository are intended to be open and reusable for educational and research purposes.