Model-Based Machine Learning for Optimizing Data Analytics
(Research Associate, University College Research Associate, University College)
Model-based machine learning advocates for using probabilistic models to learn patterns on data. Probabilistic programming
languages are the key frameworks for expressing such machine learning models. The aim of this project is to use these frameworks
for optimizing data analytics workloads. More specifically, the focus of this project is on finding more precise estimates
for cardinalities of intermediate relations in database queries, using probabilistic programming languages.