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Training Workshop: Uncertainty Estimation in AI

Starts: 2026/11/03 at 09:30

Ends: 2026/11/04 at 13:00

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Neural networks have had success in science and engineering predictive tasks such as computer vision, biology, medicine, and weather. To safely use (make decisions based on) the predictions of neural networks, the predictions need to have reliable uncertainty estimates. Yet, despite their importance, reliable predictive uncertainty estimates are often overlooked in practice. This might be due to the fact that producing such estimates is difficult. By providing an honest overview of modern uncertainty estimation methods, the participants can decide whether to adopt them in their own research.

The aim of the workshop is two-fold. First, to give a gentle introduction to what uncertainty is, the how to estimate it, and how to evaluate it in the context of neural networks. Secondly, to have participants get hands-on experience by implementing and evaluating uncertainty methods, such as Deep Ensembles, Variational Inference, and Laplace approximations.

This workshop will run over two mornings (day 1: lectures and theory, day 2: hands on practice). The workshop is open to all postgraduate students and research staff.

More information and registration here.