david-klindt / 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

ECE-3: Python Programming for Science & Engineering

Welcome!

This is the GitHub repository for the course:

ECE-3: Python Programming for Science & Engineering.

Teaching team

From the Geometric Intelligence Lab:

• Nina Miolane: Principal Instructor
• Daniel Kunin: Co-Instructor
• David Klindt: Co-Instructor
• Bongjin Koo: 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.

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 by Downey.
• Unit 02-03: Introduction to Applied Linear Algebra, by Boyd & Vandenberghe.
• Unit 04: Introduction to Statistical Learning by James, Witten, Hastie, Tibshirani, Taylor.

The textbooks are freely available online.

Syllabus

More details are on the syllabus.

Best wishes for the new quarter! ☺