Num Methods for Data Anal

An introduction to the mathematical and computational foundations of data analysis with linear models. Develops theoretical and computational understanding of numerical linear algebra algorithms for problems including data fitting, data classification, clustering, and data reduction. Focus includes vector spaces, matrix factorization, least squares methods, and singular value decompositions. Illustrations on a variety of engineering applications, including power networks, autonomous and electric vehicles, quantum computing, medical imaging, and systems biology.