Teaching Uncertainty Quantification in Machine Learning through Use Cases
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Abstract (arXiv)
Uncertainty in machine learning is not generally taught as general knowledge in Machine Learning course curricula. In this paper we propose a short curriculum for a course about uncertainty in machine learning, and complement the course with a selection of use cases, aimed to trigger discussion and let students play with the concepts of uncertainty in a programming setting. Our use cases cover the concept of output uncertainty, Bayesian neural networks and weight distributions, sources of uncertainty, and out of distribution detection. We expect that this curriculum and set of use cases motivates the community to adopt these important concepts into courses for safety in AI.
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References · 8
- 2017On Calibration of Modern Neural Networkscited by 4,960
- 2015Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learningcited by 4,448
- 2016Simple and Scalable Predictive Uncertainty Estimation using Deep Ensemblescited by 3,926
- 2017What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?cited by 2,352
- 2021A Survey of Uncertainty in Deep Neural Networkscited by 680
- 2017A Conceptual Introduction to Hamiltonian Monte Carlocited by 209
- 2019DropConnect Is Effective in Modeling Uncertainty of Bayesian Deep Networkscited by 92
- 2021I Find Your Lack of Uncertainty in Computer Vision Disturbingcited by 16
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