Proposal for New "ml" Module in SymPy for Symbolic Machine Learning Operations

[Shiva Bilavath]

Dear SymPy Development Team,

I hope this email finds you well.

My name is Shiva Bilavath, and I am a engineering final year student at Vaagdevi College of Engineering, Warangal. As I am a Python developer and an enthusiast of symbolic computation. I have been working with SymPy extensively and truly appreciate the capabilities it offers for symbolic mathematics.

I would like to propose an idea for a new module in SymPy, tentatively named sympy.ml.
The goal of this module would be to provide symbolic tools for machine learning operations, including:

• Symbolic calculation of gradients, Hessians, and Jacobians.

• Symbolic definitions of popular loss functions like MSE and cross-entropy.

• Symbolic backpropagation mechanisms (similar to automatic differentiation).

• Support for optimization expressions like Lagrange multipliers and KKT conditions.

• Simplification of symbolic machine learning proofs and derivations.

I believe this addition would bridge symbolic computation and machine learning in a novel way and could benefit researchers, educators, and developers working at the intersection of AI and mathematics.

I would be very happy to discuss this idea further, contribute to its development, or help draft an initial prototype if the team finds it promising.

Thank you for considering this proposal.
Looking forward to your thoughts!

Best regards,
Shiva Bilavath
+91 7013407805