Fall 2022: Randomized Numerical Linear Algebra in Machine Learning

Randomized Numerical Linear Algebra (RandNLA) describes a suite of algorithms which use randomness to construct small representations (sketches) of large data matrices. These sketches are then used to efficiently solve large-scale matrix problems at the core of many scientific, data science and machine learning tasks. This course will focus on algorithmic and theoretical foundations of RandNLA, including such topics as randomized dimensionality reduction and approximate matrix multiplication, as well as recent advances in the area with a particular focus on its applications to machine learning.