Learning in the Physical World

Learning in the Physical World

Abstract

I present how machine learning tasks in industrial settings differ from typical applications on the internet. As they require explicit handling of uncertainties and the incorporation of expert knowledge, the Bayesian paradigm is a good fit to formulate models.

Type
Publication
AAAI Fall Symposium Series 2019: Human-Centered AI
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Markus Kaiser
PhD candidate in Bayesian Machine Learning

My research interests include hierarchical Bayesian modelling, Gaussian Processes and scalable Bayesian Inference.