Learn · Matrix Calculus for Machine Learning and Beyond
Lecture 3 of 17Lecture Videos
Lecture 2 Part 1: Derivatives in Higher Dimensions: Jacobians and Matrix Functions
Prof. Alan Edelman and Prof. Steven G. Johnson · Massachusetts Institute of Technology
Credits and licence
- From
- Matrix Calculus for Machine Learning and Beyond, Massachusetts Institute of Technology (at MIT OpenCourseWare)
- Licence
- CC BY-NC-SA 4.0, as MIT OpenCourseWare states it. Non-commercial use only.
- Attribution
- Prof. Alan Edelman, Prof. Steven G. Johnson. Matrix Calculus for Machine Learning and Beyond. January IAP 2023. Massachusetts Institute of Technology: MIT OpenCourseWare, https://ocw.mit.edu/. License: Creative Commons BY-NC-SA.
- Who can watch
- Tweens profiles and older. How courses are placed
All the lectures
- Lecture 1 Part 1: Introduction and Motivation
- Lecture 1 Part 2: Derivatives as Linear Operators
- Lecture 2 Part 1: Derivatives in Higher Dimensions: Jacobians and Matrix Functions
- Lecture 2 Part 2: Vectorization of Matrix Functions
- Lecture 3 Part 1: Kronecker Products and Jacobians
- Lecture 3 Part 2: Finite-Difference Approximations
- Lecture 4 Part 1: Gradients and Inner Products in Other Vector Spaces
- Lecture 4 Part 2: Nonlinear Root Finding, Optimization, and Adjoint Gradient Methods
- Lecture 5 Part 1: Derivative of Matrix Determinant and Inverse
- Lecture 5 Part 2: Forward Automatic Differentiation via Dual Numbers
- Lecture 5 Part 3: Differentiation on Computational Graphs
- Lecture 6 Part 1: Adjoint Differentiation of ODE Solutions
- Lecture 6 Part 2: Calculus of Variations and Gradients of Functionals
- Lecture 7 Part 1: Derivatives of Random Functions
- Lecture 7 Part 2: Second Derivatives, Bilinear Forms, and Hessian Matrices
- Lecture 8 Part 1: Derivatives of Eigenproblems
- Lecture 8 Part 2: Automatic Differentiation on Computational Graphs