Overview

This is a hands-on class surveying a range of mathematical methods for data science, with an emphasis on solving problems with cutting-edge software packages.  An understanding of basic linear algebra and probability is needed, as well as programming skills. Python will be used.

Slides

1. Introduction
2. Classical linear algebra software
3. Linear algebra highlights
4. Norms, Distances, and Statistics
5. Singular Value Decomposition
6. Vectorization and Tensors
7. Networks and graph spectral methods
8. Gaussian graphical models
9. Matrix Calculus
10. Gradient descent optimization
11. Computational graphs with Tensorflow

yet more scalable numerical math software
PyTorch tutorial
Tensorflow tutorial