Learning With Model Uncertainty

Summary

For full set of play lists see: users.ece.cmu.edu/~koopman/lectures/index.html. 00:00:00 - Introduction 00:00:15 - Neural networks are infamous for making wrong predictions with high confidence. Ideally, when a Dropout as a Bayesian Approximation: Representing Gaussian process regression (GPR) is a probabilistic approach to making predictions. GPRs are easy to implement, flexible, and ... Dr. Bruce Wilson, University of Minnesota's Department of Bioproducts and Biosystems Engineering, discusses In-Koo Cho University of Illinois at Urbana-Champaign, USA. This module addresses the challenging area of Linear Control Course Chapter 23: Many current autonomous systems are being designed with a strong reliance on black box predictions from deep neural networks ... Channel's GitHub page hosting Jupyter Notebook: github.com/mtorabirad/MLBoost In this video, we explore the concept of ... Arthur Van Camp Carnegie Mellon and Ghent University November 27, 2018 at Rutgers Foundations of Probability Choice ...