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18.S096 | January IAP 2022 | Undergraduate

# Matrix Calculus for Machine Learning and Beyond

#### Course Description

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 …

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.

## Course Info

##### Instructors

- Prof. Alan Edelman
- Prof. Steven G. Johnson

##### Departments

- Mathematics

##### Topics

- Mathematics
  - Applied Mathematics
  - Calculus
  - Linear Algebra

##### Learning Resource Types

notes Lecture Notes
assignment_turned_in Problem Sets with Solutions
Readings
What is the derivative of determinant A? (Image by MIT OpenCourseWare.)

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