Matrix Calculus for Machine Learning and Beyond | Mathematics | MIT OpenCourseWare

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Mathematics

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January IAP 2023

Level

Undergraduate

Course Info

Instructors

Departments

Mathematics

As Taught In

January IAP 2023

Level

Undergraduate

Topics

Mathematics

Topics

Science & Math
- Mathematics

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Editable Files

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Lecture Notes

Lecture 1 Part 1: Introduction

Lecture 1, Part 1: Introduction to Matrix Calculus

Lecture 1, Part 2: Derivatives as Linear Operators

Lecture 2, Part 2: Jacobians of Matrix Functions

Lecture 3, Part 2: Finite-Difference Approximations

Lecture 4 Part 2: Nonlinear Root-Finding, Optimization, and Adjoint-Method Differentiation

Lecture 4, Part 1: Derivatives in General Vector Spaces

Lecture 4, Part 2: Nonlinear Root-Finding, Optimization, and Adjoint Differentiation

Lecture 5, Part 1: Derivative of Matrix Determinant and Inverse

Lecture 5, Part 3: Forward and Reverse-Mode Automatic Differentiation

Lecture 6 Part 1: An Introduction to (Local) Sensitivity Analysis for (Ordinary) Differential Equations

Lecture 6, Part 1a: Differentiating ODE solutions

Lecture 6, Part 1b: Calculus of Variations

Lecture 7 Part 1: Derivatives of Random Functions

Lecture 7, Part 1: Derivatives of Random Functions

Lecture 7, Part 2: Second Derivatives, Bilinear Maps, and Hessian Matrices

Lecture 8, Part 1: Derivatives of Eigenproblems

Lecture 8, Part 3: Where We Go From Here

Course Info

Instructors

Departments

Mathematics

As Taught In

January IAP 2023

Level

Undergraduate

Course Info

Instructors

Departments

Mathematics

As Taught In

January IAP 2023

Level

Undergraduate

Topics

Mathematics

Topics

Science & Math
- Mathematics

Learning Resource Types

Editable Files

Instructor Insights

Lecture Notes

Lecture Videos

Problem Set Solutions

Problem Sets

Readings

Download Course

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