Bayesian Optimization for Machine Learning : A Practical Guidebook
This is an incremental resource aimed at machine learning practitioners to simplify the use of Bayesian optimization in engineering systems.
The paper tackles the challenge of applying Bayesian optimization in machine learning by providing a practical guidebook for practitioners, demonstrating its benefits through four example problems solved with open-source libraries.
The engineering of machine learning systems is still a nascent field; relying on a seemingly daunting collection of quickly evolving tools and best practices. It is our hope that this guidebook will serve as a useful resource for machine learning practitioners looking to take advantage of Bayesian optimization techniques. We outline four example machine learning problems that can be solved using open source machine learning libraries, and highlight the benefits of using Bayesian optimization in the context of these common machine learning applications.