MIT 6.S191: Introduction to Deep Learning - MIT 6.S191: Evidential Deep Learning and Uncertainty

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!!