Learn · Matrix Calculus for Machine Learning and Beyond

Lecture 6 of 17Lecture Videos

Lecture 3 Part 2: Finite-Difference Approximations

Prof. Alan Edelman and Prof. Steven G. Johnson · Massachusetts Institute of Technology

At MIT OpenCourseWare Transcript

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.
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All the lectures

  1. Lecture 1 Part 1: Introduction and Motivation Video
  2. Lecture 1 Part 2: Derivatives as Linear Operators Video
  3. Lecture 2 Part 1: Derivatives in Higher Dimensions: Jacobians and Matrix Functions Video
  4. Lecture 2 Part 2: Vectorization of Matrix Functions Video
  5. Lecture 3 Part 1: Kronecker Products and Jacobians Video
  6. Lecture 3 Part 2: Finite-Difference Approximations Video
  7. Lecture 4 Part 1: Gradients and Inner Products in Other Vector Spaces Video
  8. Lecture 4 Part 2: Nonlinear Root Finding, Optimization, and Adjoint Gradient Methods Video
  9. Lecture 5 Part 1: Derivative of Matrix Determinant and Inverse Video
  10. Lecture 5 Part 2: Forward Automatic Differentiation via Dual Numbers Video
  11. Lecture 5 Part 3: Differentiation on Computational Graphs Video
  12. Lecture 6 Part 1: Adjoint Differentiation of ODE Solutions Video
  13. Lecture 6 Part 2: Calculus of Variations and Gradients of Functionals Video
  14. Lecture 7 Part 1: Derivatives of Random Functions Video
  15. Lecture 7 Part 2: Second Derivatives, Bilinear Forms, and Hessian Matrices Video
  16. Lecture 8 Part 1: Derivatives of Eigenproblems Video
  17. Lecture 8 Part 2: Automatic Differentiation on Computational Graphs Video