Physics-Inspired Machine Learning

Author: Ardavan Borzou, PhD

The entire field of machine learning & deep learning can be interpreted through just one equation: \[P=\frac{e^{-F}}{Z}\] And, we always need to ask a single question: "How do we find \(F\) using data?"
In the courses below, we will answer this question for various machine learning models.

Start below & learn for FREE

or Apply to our BootCamp!

Machine Learning & Deep Learning Courses:

Here’s your roadmap:

👣 Start from the top and move down, one row at a time.
  1. 🚀 Take the Course on Google Colab.
  2. 🎯 Run the Intermediate Project on Google Colab.
  3. 🌟 Add complexities of professional setting to intermediate projects and prepare them for deployment:
    • Publish them on your GitHub and build your online presence.
    • Here is an example advanced project ready for deployment.
    • In case you need help with an individual project, click "Get this Project Professionally SetUp"
  4. 🎓 Want to learn it all & even more systematically? Apply to our BootCamp
Courses Intermediate Projects Advanced Projects
Linear Regression Take me to project! Get this Project Professionally SetUp


Research Notes & Other Optional Projects:

Notes Subjects
Ep1. How the Canonical Ensemble Becomes Linear Regression Linear Regression
Ep2. How Entropy Becomes the Loss Function of Linear Regression Loss Function
Ep3. Physic's Inspired Residual Sum of Squares Residual Sum of Squares
Ep4. Gradient Descent Gradient Descent
Ep5. The Heisenberg "Principle" of Machine Learning Bias Variance TradeOff
Ep6. The Lagrange Multiplier “Method” of Machine Learning Regularization
Ep7. How do we find the (N, V, E) set in machine learning? Feature Engineering and Selection
Ep8. The physics dualities of machine learning Probabilistic basis of ML
Ep9. The "Quantum Interactions" of Machine Learning Polynomial Regression
Ep10. The "Energy Levels" of Machine Learning Logistic Regression
Ep11. The "Quantum Statistics" of Machine Learning Multinomial Logistic Regression
Ep12. Is Physics' Approach Next Machine Learning Breakthrough? Support Vector Machine (SVM)
Ep13. The Physics in a Coffee Cup can Predict in Machine Learning Kernels
Ep14. Can We Use Physics's "Symmetries" in Machine Learning? K-Nearest Neighbors (KNN)
Ep15. This Physics Game can Train Machine Learning Decision tree
Ep16. This Quantum Concept Helped Me Understand Machine Learning KMeans & Gaussian Mixture
Ep17. I Built a Mini "GPT DALL-E" in One Day On a Laptop without GPU Kernel Density Estimator

How to cite these notes:

If you use or reference material from this collection, please cite as:

Borzou, Ardavan. 2025. Physics-Inspired Machine Learning. CompuFlair. https://compu-flair.com/physics-inspired-ml

BibTeX: 

@misc{borzou2025mlphysics,
author = {Borzou, Ardavan},
title = {Physics-Inspired Machine Learning},
year = { 2025 },
url = {https://compu-flair.com/physics-inspired-ml},
note = {Accessed: 2025-12-07"}
}