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A Bayesian Approach to Machine Learning

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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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