MIT 18.S096 Matrix Calculus For Machine Learning And Beyond, IAP 2023 (English)

   Friday, November 29, 2024   

Starting a new Lecture Notes Series on MIT 18.S096 Matrix Calculus For Machine Learning And Beyond, IAP 2023

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Lecture 1: Lecture 1 Part 1: Introduction and Motivation

Lecture 2: Lecture 1 Part 2: Derivatives as Linear Operators

Lecture 3: Lecture 2 Part 1: Derivatives in Higher Dimensions: Jacobians and Matrix Functions

Lecture 4: Lecture 2 Part 2: Vectorization of Matrix Functions

Lecture 5: Lecture 3 Part 1: Kronecker Products and Jacobians

Lecture 6: Lecture 3 Part 2: Finite-Difference Approximations

Lecture 7: Lecture 4 Part 1: Gradients and Inner Products in Other Vector Spaces

Lecture 8: Lecture 4 Part 2: Nonlinear Root Finding, Optimization, and Adjoint Gradient Methods

Lecture 9: Lecture 5 Part 1: Derivative of Matrix Determinant and Inverse

Lecture 10: Lecture 5 Part 2: Forward Automatic Differentiation via Dual Numbers

Lecture 11: Lecture 5 Part 3: Differentiation on Computational Graphs

Lecture 12: Lecture 6 Part 1: Adjoint Differentiation of ODE Solutions

Lecture 13: Lecture 6 Part 2: Calculus of Variations and Gradients of Functionals

Lecture 14: Lecture 7 Part 1: Derivatives of Random Functions

Lecture 15: Lecture 7 Part 2: Second Derivatives, Bilinear Forms, and Hessian Matrices

Lecture 16: Lecture 8 Part 1: Derivatives of Eigenproblems

Lecture 17: Lecture 8 Part 2: Automatic Differentiation on Computational Graphs