https://github.com/huaxiaz/honor_engineering_analysis

This repository is adapt from the course materials for Honors Engineering Analysis at Northwestern University. The course is designed for Engineering first-year undergraduate students to learn about linear algebra and its applications.
https://github.com/huaxiaz/honor_engineering_analysis

degree-distribution erdos-renyi-model iterative-methods jacobi-iteration jupyter-notebook linear-algebra markov-chain networkx networkx-tutorial numpy numpy-matrix pagerank-algorithm python random-graphs randomgraph

Last synced: 21 days ago
JSON representation

This repository is adapt from the course materials for Honors Engineering Analysis at Northwestern University. The course is designed for Engineering first-year undergraduate students to learn about linear algebra and its applications.

• Host: GitHub
• URL: https://github.com/huaxiaz/honor_engineering_analysis
• Owner: huaxiaz
• License: mit
• Created: 2024-09-20T19:26:06.000Z (about 2 months ago)
• Default Branch: main
• Last Pushed: 2024-10-15T15:02:02.000Z (23 days ago)
• Last Synced: 2024-10-16T16:04:49.278Z (22 days ago)
• Topics: degree-distribution, erdos-renyi-model, iterative-methods, jacobi-iteration, jupyter-notebook, linear-algebra, markov-chain, networkx, networkx-tutorial, numpy, numpy-matrix, pagerank-algorithm, python, random-graphs, randomgraph
• Language: Jupyter Notebook
• Homepage:
• Size: 623 KB
• Stars: 1
• Watchers: 0
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

README

        
# Honor_Engineering_Analysis

This repository organizes the course materials I am teaching at Northwestern University (Fall 2024). The course is designed for Engineering first-year undergraduate students to learn about linear algebra and its applications (Honor Level). Please note the materials are revised by myself with an emphasis on [**Python (Anaconda Distribution)**](https://www.anaconda.com/download), so they are not exactly the same as the original course materials (The course is taught with an emphasis on [**MATLAB**](https://www.mathworks.com/products/matlab.html) implementation). 

- Python_basics Folder

  - Python_JupyterNotebook: this tutorial notebook provides a quick overview to Python programming. If you're new to programming, this is the perfect starting point.

  - NumPy_Matrix: this tutorial notebook covers essential concepts from Linear Algebra  (Chapter 1-4 from ***Linear Algebra and its Applications (5th Edition)***) and demonstrates the implementation using [**NumPy**](https://numpy.org).

  - NumPy_Vector: this tutorial notebook covers essential concepts from Vector Analysis (Chapter 6 from ***Linear Algebra and its Applications (5th Edition)***) and demonstrates the implementation using [**NumPy**](https://numpy.org).

- Case_studies Folder

  - Graph_PageRank: this notebook covers basics about graph powered by [**NetworkX**](https://networkx.org) and PageRank Algorithm (Case Study 1 & Week 1).

  - Random_Graph: this notebook covers degree distributions of graphs and different types of random graphs (Week 2).

  - Iterative_methods: this notebook covers two common iterative algorithms (Week 3).

- Further_reading Folder: this folder contains a markdown file that primarily lists journal publications in the relevant research areas.