Learn how to model real problems with probabilistic programming
August 6th, 2018 (9a.m. - 6p.m.)
Links to part of the course materials are available on MLTRAIN.
Time | Event |
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09:00am - 09:45am | Golnoosh Farnadi and Eriq Augustine: Introduction to Probabilistic Soft Logic |
09:45am - 10:30am | Eriq Augustine: Getting Started with Probabilistic Soft Logic |
10:50am - 11:35am | Golnoosh Farnadi: Collective Classification and Link Prediction |
11:35am - 12:20pm | Eriq Augustine: Entity Resolution |
02:05pm - 02:50pm | Eli Bingham: Introduction to Pyro, Models/Inference |
02:50pm - 03:35pm | Eli Bingham: Bayesian Regression |
04:00pm - 04:45pm | Noah Goodman: Variational Auto-encoders |
04:45pm - 05:30pm | Noah Goodman: Building on the VAE: recipes for missing and sequential data (semisupervised VAE, DMM) |
05:30pm - 05:55pm | Hyungil Ahn: Applying Deep Probabilistic Programming to Enterprise AI |
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:
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.
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* UAI offers some very compelling sponsorship packages for big companies and startups. For more information and custom packages contact us at uai2018chairs@gmail.com ** 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.