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