This lectures discusses how the SVD captures dominant correlations in a matrix of data.
These lectures follow Chapter 1 from: "Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control" by Brunton and Kutz
Amazon: https://www.amazon.com/Data-Driven-Science-Engineering-Learning-Dynamical/dp/1108422098/
Book Website: http://databookuw.com
Book PDF: http://databookuw.com/databook.pdf
Brunton Website: http://eigensteve.com
This video was produced at the University of Washington