Bayesian Optimization and Data Science

• Main Authors: Archetti, Francesco (Author, http://id.loc.gov/vocabulary/relators/aut), Candelieri, Antonio (http://id.loc.gov/vocabulary/relators/aut)
• Corporate Author: SpringerLink (Online service)
• Format: Electronic eBook
• Language: English
• Published: Cham : Springer International Publishing : Imprint: Springer, 2019.
• Edition: 1st ed. 2019.
• Series: SpringerBriefs in Optimization,
• Subjects: Operations research.; Management science.; Machine learning.; Computer software.; Statistics .; Operations Research, Management Science.; Machine Learning.; Mathematical Software.; Bayesian Inference.

This volume brings together the main results in the field of Bayesian Optimization (BO), focusing on the last ten years and showing how, on the basic framework, new methods have been specialized to solve emerging problems from machine learning, artificial intelligence, and system optimization. It al...

Table of Contents:

• 1. Automated Machine Learning and Bayesian Optimization
• 2. From Global Optimization to Optimal Learning
• 3. The Surrogate Model
• 4. The Acquisition Function
• 5. Exotic BO
• 6. Software Resources
• 7. Selected Applications.