/linalg-for-ds

Linear Algebra and Optimization for Data Science

Primary LanguageJupyter Notebook

Optimization and Computational Linear Algebra for Data Science

This repository contains some notes for the course, as well as the homeworks. These materials are not meant to be proper lecture notes. They only contain the main results from the course. Examples, figures, and many proofs are missing.

You can download all the notes here in .zip format.

Outline

  1. Vector spaces
  2. Linear transformations
  3. Rank
  4. Norm and inner product
  5. Matrices and orthogonality
  6. Eigenvalues, eigenvectors and Markov chains
  7. The spectral theorem and PCA
  8. Graphs and Linear Algebra
  9. Convex functions
  10. Linear regression
  11. Optimality conditions
  12. Gradient descent