Linear Algebra - Computational Projects

This module contains computational projects developed to complement a traditional, semester-long first course in linear algebra for undergraduate students.

We use python + numpy (a numerical linear algebra package) as the main computational tools, along with Jupyter notebooks / Google Colaboratory. No prior programming experience is expected or needed from students or teachers.

The textbook that was used for the course when the module was developed was Gilbert Strang’s Introduction to Linear Algebra. However, the projects here could go with other introductory linear algebra courses even if a different textbook is used.

Learning Goals

The main goal of the projects is to help students get a hands-on experience with real applications of linear algebra. In addition, the projects introduce students to modern computational tools for linear algebra computation (python, numpy, Jupyter notebooks). No prior programming experience is expected or needed.

Module Content Overview

• Reference and Demonstration Materials
  • These are introductory materials to be provided or demostrated to students before the projects are assigned
• Computational Projects
  • Each project is self-contained in the form of a Jupyter notebook / Google Colaboratory file
  • Python + numpy are used in all projects
  • Three Projects
    1. Modeling change in population distributions as a matrix equation
    2. Understanding linear regression and least-squares solutions
    3. Using PageRank to analyze co-appearance of characters in a work of fiction

Module Access and Download

Google Drive and Google Colaboratory

or

github

Acknowledgements

Not all of the projects were developed from scratch. Some of them were adapted from other resources, included below.

References

John Goulet, Linear Algebra Projects: Project 4: Urban Population Dynamics, https://users.wpi.edu/~goulet/ph/proj4.htm