Uncertainty Quantification for Bayesian Inverse Problems - Efficient Methods in the Small Noise Regime
Offered By: Isaac Newton Institute for Mathematical Sciences via YouTube
Course Description
Overview
Explore uncertainty quantification for Bayesian inverse problems in this Kirk Lecture delivered by Professor Claudia Schillings from Freie Universität Berlin. Delve into efficient methods for the small noise regime as part of the programme on the mathematical and statistical foundation of future data-driven engineering. Gain insights into advanced mathematical concepts and their applications in engineering during this 57-minute talk presented at the Isaac Newton Institute for Mathematical Sciences.
Syllabus
Date: 11 April 2023 - 16:00 to
Taught by
Isaac Newton Institute for Mathematical Sciences
Related Courses
Introduction to Artificial IntelligenceStanford University via Udacity Natural Language Processing
Columbia University via Coursera Probabilistic Graphical Models 1: Representation
Stanford University via Coursera Computer Vision: The Fundamentals
University of California, Berkeley via Coursera Learning from Data (Introductory Machine Learning course)
California Institute of Technology via Independent