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