Koopman Operator Theory Based Machine Learning of Dynamical Systems
Offered By: Fields Institute via YouTube
Course Description
Overview
Explore Koopman operator theory and its applications in machine learning for dynamical systems in this lecture by Igor Mezic from the University of California. Delivered as part of the Third Symposium on Machine Learning and Dynamical Systems at the Fields Institute, delve into advanced concepts at the intersection of operator theory and data-driven modeling. Gain insights into how Koopman operators can enhance understanding and prediction of complex dynamical systems, with potential applications across various scientific and engineering domains.
Syllabus
Koopman Operator Theory Based Machine Learning of Dynamical Systems
Taught by
Fields Institute
Related Courses
Social Network AnalysisUniversity of Michigan via Coursera Intro to Algorithms
Udacity Data Analysis
Johns Hopkins University via Coursera Computing for Data Analysis
Johns Hopkins University via Coursera Health in Numbers: Quantitative Methods in Clinical & Public Health Research
Harvard University via edX