MIT Introduction to Deep Learning 6.S191: Lecture 7
Evidential Deep Learning and Uncertainty Estimation
Lecturer: Alexander Amini
January 2021

For all lectures, slides, and lab materials: http://introtodeeplearning.com​

Lecture Outline
0:00​ Introduction and motivation
5:00​ Outline for lecture
5:50 Probabilistic learning
8:33 Discrete vs continuous target learning
14:12 Likelihood vs confidence
17:40 Types of uncertainty
21:15 Aleatoric vs epistemic uncertainty
22:35 Bayesian neural networks
28:55 Beyond sampling for uncertainty
31:40 Evidential deep learning
33:29 Evidential learning for regression and classification
42:05 Evidential model and training
45:06 Applications of evidential learning
46:25 Comparison of uncertainty estimation approaches
47:47 Conclusion


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