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A lightweight Unity asset for running powerful Bayesian optimization. It supports practical human-in-the-loop workflows where the optimizer proposes parameter values, collects user feedback as objective scores, updates the model, and recommends the next design iteration.
A concept-first repository of Jupyter notebooks tracing a path from PyTorch fundamentals to Bayesian Optimisation, designed both as a structured learning journey and as clear, self-contained tutorials for others.
This repository combines Machine Learning with the Variational Quantum Eigensolver (VQE), a hybrid quantum-classical algorithm for estimating ground state energies in quantum systems, useful in quantum chemistry and physics.
Interactive Bayesian optimization for expensive black-box functions, with a human in the loop. Developed as a capstone project for the Imperial College Machine Learning & AI certificate.