Robust principal-component analysis (RPCA): applications of machine learning

And we conclude our series on machine learning!! After discussing optimal sensor placement, and QR pivoting, today we talk about robust PCA, with the aim of increasing the robustness against noisy/faulty data. Additional information in the excellent book by Steve Brunton and Nathan Kutz: https://www.databookuw.com/ I also acknowledge Scott Dawson for his input on this material.