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Model-Based Machine Learning for Optimizing Data Analytics

Supervisor

Amir Shaikhha
(Research Associate, University College Research Associate, University College)

Suitable for

MSc in Computer Science
Mathematics and Computer Science, Part C
Computer Science and Philosophy, Part C
Computer Science, Part C
Computer Science, Part B

Abstract

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.