MIT 6.S191: Evidential Deep Learning and Uncertainty

MIT 6.S191: AI Bias and Fairness

MIT 6.S191: Reinforcement Learning

Prof. Claudia Schillings | Kirk Lecture: Uncertainty quantification for Bayesian inverse problems...

MIT 6.S191: Deep CPCFG for Information Extraction

Day 5-Understanding CNN &Impementation| Live Deep Learning Community Session

MIT Introduction to Deep Learning | 6.S191

MIT 6.S191 (2021): Reinforcement Learning

6. Monte Carlo Simulation

MIT 6.S191 (2023): Deep Generative Modeling

MIT 6.S191 (2020): Recurrent Neural Networks

16. Learning: Support Vector Machines

All Machine Learning algorithms explained in 17 min

2023 5.2 Bayesian Learning and Uncertainty Quantification - Eric Nalisnick

MIT 6.S191 (2022): Reinforcement Learning

CS 285: Lecture 11, Part 3

MIT 6.S191 (2021): Deep Learning New Frontiers

MIT 6.S091: Introduction to Deep Reinforcement Learning (Deep RL)

Chris Fonnesbeck - Probabilistic Python: An Introduction to Bayesian Modeling with PyMC

MIT Introduction to Deep Learning (2023) | 6.S191

Bridging Computation and Experimentation with Evidential Deep Learning | Ava Amini