Opening book details…
Can I read Bayesian Optimization and Data Science on EtoBox?
Bayesian Optimization and Data Science by Francesco Archetti, Antonio Candelieri is a nonfiction available to read on EtoBox.
What is Bayesian Optimization and Data Science about?
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.
Who reads Bayesian Optimization and Data Science?
It is typically read by self-directed learners exploring a subject in depth.
Common subject areas: history, science, philosophy, social sciences.
- Author
- Francesco Archetti, Antonio Candelieri
- Publisher
- Springer International Publishing : Imprint : Springer
- Published
- 2019
- Language
- EN
- ISBN
- 9783030244934
- Category
- nonfiction
- Subjects
- Mathematics, Business, Science
More by Francesco Archetti, Antonio Candelieri
Browse all works by Francesco Archetti, Antonio Candelieri
Similar books
- Bayesian Optimization in Action — Quan Nguyen (2023)
- Bayesian Artificial Intelligence (Chapman & Hall/Crc Computer Science and Data Analysis) — Korb, Kevin B., Nicholson, Ann E. (2004)
- Machine Learning: A Bayesian and Optimization Perspective (Net Developers) — Sergios Theodoridis (2015)
- Bayesian Optimization — Roman Garnett (2023)
- Bayesian Heuristic Approach to Discrete and Global Optimization: Algorithms, Visualization, Software, and Applications (Nonconvex Optimization and Its Applications (17)) — Jonas Mockus, William Eddy, Audris Mockus, Linas Mockus, Gintaras Reklaitis (auth.) (1997)
- Bayesian Optimization for Materials Science (SpringerBriefs in the Mathematics of Materials, 3) — Daniel Packwood (auth.) (2017)