Recent success in the domain of unsupervised and semi-supervised learning has been lately a pivotal factor for development of Physics Aware and Symmetry Aware Machine Learning techniques where a model learns the symmetry of a dataset as a meta task and ends up learning the physics through the same. This project will focus on ways to learn the symmetries using semi-supervised approaches using CMS data.