Learning Objectives:

By the end of this tutorial, you will be able to:

1. Assess and improve model calibration using temperature scaling
2. Implement heteroscedastic models to capture prediction uncertainty
3. Use MC dropout to estimate epistemic uncertainty
4. Apply conformal prediction for distribution-free uncertainty intervals
5. Distinguish between different types of uncertainty in your predictions