Getting to the Bottom of It: Numerical Methods for Machine Learning

Joe McEwen and Jesse Loi

version 0.2 (the typo edition) 2025

Help! This is an early version of the notes, if you find an error, please let us know. Thank you.

• 1. What is this?
• 2. Guidance
  • 2.1. Do You Want a Job?
  • 2.2. Good Coding
• 3. Array Basics
  • 3.1. Arrays in Numpy
  • 3.2. Dot Product
  • 3.3. Matrix Multiplication
  • 3.4. Solving Systems of Equations
  • 3.5. Should You Directly Invert a Matrix in Numerical Methods? (Hint: almost always No)
  • 3.6. Inversion for \(3\times3\) Matrices
• 4. Differential Calculus
  • 4.1. Single Variable Calculus
  • 4.2. Multivariable Calculus
  • 4.3. Automatic Differentiation with Jax
  • 4.4. Derivatives, a Helpful Lesson
• 5. Matrices
  • 5.1. Transpose, Inverse, and Norm