Bayesian Optimization and Data Science

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 also analyzes the software resources available for BO and a few selected application areas. Some areas for which new results are shown include constrained optimization, safe optimization, and applied mathematics, specifically BO's use in solving difficult nonlinear mixed integer problems.

The book will help bring readers to a full understanding of the basic Bayesian Optimization framework and gain an appreciation of its potential for emerging application areas. It will be of particular interest to the data science, computer science, optimization, and engineering communities.

Gives readers an idea of the potential of the application of Bayesian Optimization to both traditional feels and emerging ones Provides full and updated coverage of the areas of constrained Bayesian Optimization and Safe Bayesian Optimization Covers software resources, allowing readers to make informed and educated choices among the different platforms available to set up Bayesian Optimization components in academic and industrial activities Allows a full understanding of the basic algorithmic framework, including recent proposals about acquisition functions

Inhalt

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.