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Matrix Calculus (for Machine Learning and Beyond)

2025/01/07 by Paige Bright, Alan Edelman, Bright, Paige +3 · 13 voices · 2 citations
Computer Science · #Neural Networks and Applications #cs.LG #math.HO #math.NA #stat.ML

paper · pdf · doi:10.48550/arxiv.2501.14787

openalex publication_date 2025/01/07 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

This course, intended for undergraduates familiar with elementary calculus and linear algebra, introduces the extension of differential calculus to functions on more general vector spaces, such as functions that take as input a matrix and return a matrix inverse or factorization, derivatives of ODE solutions, and even stochastic derivatives of random functions. It emphasizes practical computational applications, such as large-scale optimization and machine learning, where derivatives must be re-imagined in order to be propagated through complicated calculations. The class also discusses efficiency concerns leading to "adjoint" or "reverse-mode" differentiation (a.k.a. "backpropagation"), and gives a gentle introduction to modern automatic differentiation (AD) techniques.

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