Book Review:; Algorithmic Mathematics in Machine Learning

Abstract

SIAM Review, Volume 67, Issue 4, Page 917-918, December 2025.
In the current academic landscape, nearly every mathematician will at some point be called upon to contribute—be it through teaching or research—to the burgeoning fields of data science and machine learning. Acquiring the necessary fundamentals in these areas ought to be straightforward. However, for many mathematicians, a significant language barrier arises when encountering the more computer science oriented literature. Bohn, Garcke, and Griebel tackle this challenge from a thoroughly mathematical perspective. Their notation is impeccable, consistently clarifying whether the subject at hand is a scalar, vector, matrix, or function. Concepts are introduced with unwavering rigor, distinguishing between well-posed and ill-posed problems, as well as between algorithms backed by convergence results and those that remain heuristic in nature.