Machine Learning for Physicists: Research Notes
Abstract
In these collection of notes we seek to uncover a shared foundation between physics and machine learning. Our goals are twofold: to teach machine learning through the familiar lens of physics, and to explore how insights from physical systems can inspire the development of more robust, interpretable, and efficient machine learning models.
Here’s your roadmap:
🎬 Start from Episode 1 and move through the episodes step by step 👣.- 🔹 Read the Research Notes and watch the embedded videos to deeply understand each concept.
- 🔹 Take the Course. Open the corresponding Colab notebook, run the code from top to bottom, and see how these physics-based ideas map onto conventional ML algorithms.
- 🔹 Run the Project. Apply your knowledge on real datasets, solve real-world problems, and gain hands-on experience that bridges theory and practice.
- 🌟 Ready to go even further? ✨ Turn the project into a unique-to-you professional GitHub repository — with a clean, industry-standard folder structure, production-ready settings, and deploy as a web application to showcase your work live.
Episodes
The following index mirrors the public listing.
Citation
How to cite this work:
If you use or reference material from this collection, please cite as:
Borzou, Ardavan. 2025. Machine Learning for Physicists: Research Notes. CompuFlair. https://compu-flair.com/ml-for-physicists
BibTeX:
@misc{borzou2025mlphysics,
author = {Borzou, Ardavan},
title = {Machine Learning for Physicists: Research Notes},
year = { 2025 },
url = {https://compu-flair.com/ml-for-physicists},
note = {Accessed: 2025-10-30"}
}