A Bayesian Approach to Machine Learning

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• Fergus Simpson -- PROWLER.io
• Thursday 21 November 2019, 13:00-14:30
• Kavli Large Meeting Room, Kavli Building.

If you have a question about this talk, please contact James Fergusson.

Conventional approaches to machine learning can suffer from a wide range of issues such as overfitting, poorly calibrated uncertainties, and difficulty in explaining their outputs. I will outline various steps which have been taken towards resolving these issues, by adopting a probabilistic framework. This includes some of the latest research from PROWLER .io, where we apply Bayesian inference to a wide range of machine learning problems.

This talk is part of the Data Intensive Science Seminar Series series.

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