Learning in the Physical World


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.

AAAI Fall Symposium Series 2019: Human-Centered AI
Markus Kaiser
Markus Kaiser
Research Scientist

Research Associate at the University of Cambridge and Research Scientist at Siemens AG. I am interested in scalable Bayesian machine learning and Gaussian processes.