Advanced mathematics and its applications such as machine learning (ML) or optimization would be too simple, ineffective, and quite frankly dull, if we could only work on univariate problems. Linear algebra provides the data-types, the tools, the operations, and the theory to enable the use of multivariate datasets and multivariate models.
This article presents an overview of concepts from linear algebra that are essential to achieving mastery in machine learning, deep learning, optimization, and multivariate calculus.