https://github.com/geometric-intelligence/ece3

The lectures present concepts from linear algebra, such as matrix computations, systems of linear equations, eigenspace decomposition, inner-product, orthogonality, least-squares and linear regression. Students actively engage with the materials with an introduction to Python programming
https://github.com/geometric-intelligence/ece3

Last synced: 26 days ago
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The lectures present concepts from linear algebra, such as matrix computations, systems of linear equations, eigenspace decomposition, inner-product, orthogonality, least-squares and linear regression. Students actively engage with the materials with an introduction to Python programming

• Host: GitHub
• URL: https://github.com/geometric-intelligence/ece3
• Owner: geometric-intelligence
• License: mit
• Created: 2021-09-22T20:07:31.000Z (over 3 years ago)
• Default Branch: main
• Last Pushed: 2023-12-07T20:43:24.000Z (over 1 year ago)
• Last Synced: 2025-04-09T02:12:42.666Z (about 2 months ago)
• Language: Jupyter Notebook
• Homepage:
• Size: 241 MB
• Stars: 16
• Watchers: 4
• Forks: 35
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# ECE-3: Python Programming for Science & Engineering

Welcome!

This is the GitHub repository for the course:

ECE-3: Python Programming for Science & Engineering.

![alt text](https://github.com/geometric-intelligence/ece3/blob/main/lectures/figs/00_signal_processing.jpeg?raw=true)

### Teaching team

From the [Geometric Intelligence Lab](https://gi.ece.ucsb.edu/):

- [Nina Miolane](https://www.ece.ucsb.edu/people/faculty/nina-miolane): Principal Instructor

- [Daniel Kunin](https://daniel-kunin.com/): Co-Instructor

- [David Klindt](https://david-klindt.github.io/): Co-Instructor

- [Bongjin Koo](https://bongjinkoo.github.io/): Co-Instructor

TAs (Fall 2023): Aaditya Prakash Kattekola, Arghavan Zibaie, Zihu Wang, Jesse Lee, Karthik Somayaji Nanjangud Suryanarayana, Yuxuan Yin.

### Interact with the course contents

You can access and run the lecture slides and lab notebooks by clicking on the Binder link below.

[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/geometric-intelligence/ece3/main?filepath=lectures)

### Outline

- Unit 01: Welcome to Python

- Unit 02: Computing with Data in Python

- Unit 03: Summarizing Data in Python

- Unit 04: Predicting from Data with Machine Learning in Python

### Textbooks

The content of this class relies on the following excellent textbooks:

- Unit 01: [Think Python](https://greenteapress.com/wp/think-python-2e/) by Downey.

- Unit 02-03: [Introduction to Applied Linear Algebra](https://web.stanford.edu/~boyd/vmls/vmls.pdf), by Boyd & Vandenberghe.

- Unit 04: [Introduction to Statistical Learning](https://www.statlearning.com/) by James, Witten, Hastie, Tibshirani, Taylor.

The textbooks are freely available online. 

### Syllabus

More details are on the [syllabus](https://github.com/geometric-intelligence/ece3/blob/main/ece3_syllabus.pdf).

Best wishes for the new quarter! ☺