Numerical Linear Algebra (NLA) procedures are among the primary building blocks of machine learning algorithms. Several key concepts originating in NLA have subsequently been adopted as first class citizens in the Machine Learning (ML) literature. Conversely, machine learning problems are posing new mathematical and algorithmic questions for the NLA community. This workshop will seek to foster this deep relationship further, with the core belief that in this age of big and complex data, it is no longer optimal for NLA kernels to be treated opaquely as a blackbox in ML applications. Topics of interest include, but are not limited to, the following:
