Applying machine learning and optimization to high-throughput experimentation

Applying machine learning and optimization to high-throughput experimentation

This study investigates the effects of different hyperparameters and settings in performance of Bayesian and Particle Swarm Optimization, and in doing so develop best practices for their practical application. Concrete Compressive Strength Data Set was modelled with Xtreme Gradient Boosting Regresso...

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Bibliographic Details
Main Author: Low, Andre Kai Yuan
Other Authors: Kedar Hippalgaonkar
Format: Final Year Project
Language:English
Published: Nanyang Technological University 2021
Subjects:
Engineering::Materials
Engineering::Computer science and engineering::Computing methodologies::Simulation and modeling
Online Access:https://hdl.handle.net/10356/147754
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Institution: Nanyang Technological University
Language: English
Description
Summary:This study investigates the effects of different hyperparameters and settings in performance of Bayesian and Particle Swarm Optimization, and in doing so develop best practices for their practical application. Concrete Compressive Strength Data Set was modelled with Xtreme Gradient Boosting Regressor, with results being compared against a benchmark of Random Search. Additionally, various notebooks and code was developed to provide a resource for implementing machine learning in high-throughput experiments, and designing appropriate virtual experiments through the Monte Carlo method with Latin sampling. Different plotting functions were also developed to provide better visualization of the entire process.

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