18.S096 Matrix Calculus for Machine Learning and Beyond, January IAP 2022
Name
18-s096-iap-2022.zip
Description
This package contains the same content as the online version of the course.
Size
21.49 MB
Format
ZIP
Checksum (MD5)
7a6100259ab399c4973e0831c58b0b76
Author(s)
Edelman, Alan
Johnson, Steven G.
Date Issued
2022
Abstract
We all know that calculus courses such as 18.01 Single Variable Calculus and 18.02 Multivariable Calculus cover univariate and vector calculus, respectively. Modern applications such as machine learning require the next big step, matrix calculus.
This class covers a coherent approach to matrix calculus showing techniques that allow you to think of a matrix holistically (not just as an array of scalars), compute derivatives of important matrix factorizations, and really understand forward and reverse modes of differentiation. We will discuss adjoint methods, custom Jacobian matrix vector products, and how modern automatic differentiation is more computer science than mathematics in that it is neither symbolic nor based on finite differences.
Subjects
matrix calculus
modes of differentiation
applied mathematics
calculus
linear algebra
adjoint methods
Jacobian matrix vector products
modern automatic differentiation
MIT Department
Massachusetts Institute of Technology. Department of Mathematics
Terms of Use
Attribution-NonCommercial-NoDerivs 3.0 United States
Persistent DSpace Link
