This repository contains the linear algbra chapter that I wrote for the upcoming book "Python Jupyter Notebooks for College Math Teachers."
The book is currently undergoing editing, but we expect a final copy to be available sometime in Spring 2024.
You can access the "in progress" draft HERE.
Book Editors: Paul Isihara, Peter Jantsch, Thomas VanDrunen
Technical Editor: Claire Wagner
Faculty Contributors: Soheil Anbouhi, Laura Gross, Paul Isihara, Peter Jantsch, Ying Li, Yiheng Liang, Rachel Petrik, Inne Singge
Student Contributors: Samuel Carlson, Claire Wagner, Ziling Zhong, Jonathan Zhu
The linear algebra chapter serves as introductory exploration of fundamental concepts in linear algebra with a focus on computational aspects with Python. It also serves as foundational background for the subsequent chapter, “Linear Algebra and Optimization for Data Analysis”.
Each section begins with a brief overview of the objectives and followed by several examples exercises to enhance understanding and facilitate learning. Moreover, I aim to include numerical notes wherever appropriate. These notes aim to explain and compare different computational approaches to problem-solving. My goal is to write my own code (whenever possible) to solve problems. However, for matrices of higher dimensions, we take advantage of the NumPy linear algebra module which provide efficient implementations of standard linear algebra algorithms.
🚀 What's Inside:
- Ready-to-use Jupyter notebooks for linear algebra
- Visualizations and interactive examples
- Hands-on exercises and solutions
- Practical applications for the classroom
📦 Getting Started: These notebooks are designed for motivated students and teachers! Feel free to clone this repository and explore the notebooks. Or simply click on the following links to open them in Google Colab:
-Linear Systems and their solutions
Stay tuned for the book's release, and let's empower math teachers and students with the power of Python and Jupyter!