MLTrain @UAI 2018
Learn how to model real problems with probabilistic programming
This year at UAI we are hosting a full day of hands-on training on Probabilistic Programming during the first day of the tutorials (August 6th). Learn how to model uncertainty with the next generation of machine learning tools. In this MLTrain we will cover use cases of Probabilistic programming languages like Pyro and Probabilistic Soft Logic. You will learn:
- The theory behind these languages (Inference model, algorithms)
- How to use knowledge graphs like freebase in your models
- How to incorporate user domain knowledge in your models
- How to solve relational problems like entity resolution.
- How to build generative models
- How to solve problems with small amount of data
This workshop will be classroom style, with lectures and ipython notebook exercises on real problems. Probabilistic programming languages are high-level languages that make it easy for developers to define probability models and then “solve” these models automatically.
- early bird $350 (ends June 15th)
- late bird $500
- student ticket early bird $250
- student late bird $350
- onsite $600
- onsite student $500
Find more information at the UAI website
- Nikolaos Vasiloglou (Relational AI)
- Alejandro Molina (TU-Darmstadt)
- Lise Getoor (UC Santa Cruz, D3 Research Institute)
Installing PSL and Pyro is pretty easy and it will be discussed during the class. If you are having difficulties you can install this virtual box machine (prepared by Alejandro).
You can find the videos of Probabilistic Programming with Probabilistic Soft Logic session here
You can find the videos of Porbabilistic Programming wih Pyro here.
Material for PSL
All the examples can be found on GitHub under the uai2018 branch
- Introduction to PSL
- Getting started with PSL
- Collective Classification
- Link Prediction
- Entity Resolution
- Advanced topics
Material for Pyro
** All proceedings from the event will go to AUAI, the non-profit organization that organizes the annual Conference on Uncertainty in Artificial Intelligence (UAI) and, more generally, promotes research in pursuit of advances in knowledge representation, learning, and reasoning under uncertainty.