Uploaded March 2021 | Updated September 2026, 2 weeks ago
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
Subscribe to stay up to date with new deep learning lectures at MIT, or follow us @MITDeepLearning on Twitter and Instagram to stay fully-connected!!
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
Subscribe to stay up to date with new deep learning lectures at MIT, or follow us @MITDeepLearning on Twitter and Instagram to stay fully-connected!!
