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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| Format: | Final Year Project (FYP) |
| Language: | English |
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Nanyang Technological University
2021
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| Online Access: | https://hdl.handle.net/10356/147754 |
| _version_ | 1826119132975726592 |
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| author | Low, Andre Kai Yuan |
| author2 | Kedar Hippalgaonkar |
| author_facet | Kedar Hippalgaonkar Low, Andre Kai Yuan |
| author_sort | Low, Andre Kai Yuan |
| collection | NTU |
| description | 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. |
| first_indexed | 2024-10-01T04:54:45Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/147754 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T04:54:45Z |
| publishDate | 2021 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1477542023-03-04T15:42:36Z Applying machine learning and optimization to high-throughput experimentation Low, Andre Kai Yuan Kedar Hippalgaonkar School of Materials Science and Engineering A*STAR Institute of Material Research and Engineering Lim Yee Fun kedar@ntu.edu.sg Engineering::Materials Engineering::Computer science and engineering::Computing methodologies::Simulation and modeling 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. Bachelor of Engineering (Materials Engineering) 2021-04-22T03:12:48Z 2021-04-22T03:12:48Z 2021 Final Year Project (FYP) Low, A. K. Y. (2021). Applying machine learning and optimization to high-throughput experimentation. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/147754 https://hdl.handle.net/10356/147754 en application/pdf Nanyang Technological University |
| spellingShingle | Engineering::Materials Engineering::Computer science and engineering::Computing methodologies::Simulation and modeling Low, Andre Kai Yuan Applying machine learning and optimization to high-throughput experimentation |
| title | Applying machine learning and optimization to high-throughput experimentation |
| title_full | Applying machine learning and optimization to high-throughput experimentation |
| title_fullStr | Applying machine learning and optimization to high-throughput experimentation |
| title_full_unstemmed | Applying machine learning and optimization to high-throughput experimentation |
| title_short | Applying machine learning and optimization to high-throughput experimentation |
| title_sort | applying machine learning and optimization to high throughput experimentation |
| topic | Engineering::Materials Engineering::Computer science and engineering::Computing methodologies::Simulation and modeling |
| url | https://hdl.handle.net/10356/147754 |
| work_keys_str_mv | AT lowandrekaiyuan applyingmachinelearningandoptimizationtohighthroughputexperimentation |