Toolbox: Uncertainty Quantification

• Paper: Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods

Examples

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Prelude

1. Introduction

2. Sources of uncertainty

3. Auxiliary sites on information-theory, probability theory and other mathematical concepts

4. Modeling approximation uncertainty

5. Probability estimation via scoring, calibration, and ensembles

6. Maximum likelihood estimation and Fisher information

7. Generative models

8. Gaussian processes

9. Deep neural network ensembles

10. Bayesian neural networks

11. Credal sets and classifiers

12. Reliable classification

13. Conformal prediction for classification and regression

14. Set-valued prediction based on utility maximization