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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

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

Last synced: 25 days ago
JSON representation

Host: GitHub
URL: https://github.com/huaxiaz/honor_engineering_analysis
Owner: huaxiaz
License: mit
Created: 2024-09-20T19:26:06.000Z (3 months ago)
Default Branch: main
Last Pushed: 2024-10-22T02:27:50.000Z (2 months ago)
Last Synced: 2024-12-07T07:40:19.953Z (25 days ago)
Topics: complex-numbers, complexity, degree-distribution, echelon-form, erdos-renyi-model, fractals, 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: 632 KB
Stars: 1
Watchers: 0
Forks: 0
Open Issues: 0
Metadata Files:
- Readme: README.md
- License: LICENSE

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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]((https://www.mccormick.northwestern.edu/electrical-computer/academics/courses/descriptions/206-EA-1.html)) 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).

  - Complex_dynamics:  this notebook covers an overview of complex numbers and complex dynamics (fractals) (Week 4).

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

## Acknowledgement

Thanks for valuable feedback from [Professor Randy Freeman](http://www.eecs.northwestern.edu/~freeman/)