MIT 6.S191: Introduction to Deep Learning - MIT 6.S191 (2023): Robust and Trustworthy Deep Learning

MIT Introduction to Deep Learning 6.S191: Lecture 5 Robust and Trustworthy Deep Learning Lecturer: Sadhana Lolla (Themis AI, https://themisai.io) 2023 Edition For all lectures, slides, and lab materials: http://introtodeeplearning.com​ Lecture Outline 0:00 - Introduction and Themis AI 3:46 - Background 7:29 - Challenges for Robust Deep Learning 8:24 - What is Algorithmic Bias? 14:13 - Class imbalance 16:25 - Latent feature imbalance 20:30 - Debiasing variational autoencoder (DB-VAE) 23:24 - DB-VAE mathematics 27:40 - Uncertainty in deep learning 29:50 - Types of uncertainty in AI 32:48 - Aleatoric vs epistemic uncertainty 33:29 - Estimating aleatoric uncertainty 37:42 - Estimating epistemic uncertainty 44:11 - Evidential deep learning 46:44 - Recap of challenges 47:14 - How Themis AI is transforming risk-awareness of AI 49:30 - Capsa: Open-source risk-aware AI wrapper 51:51 - Unlocking the future of trustworthy AI 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!!