Overview
- Provides the reader with an essential understanding of intelligent systems
- Does not describe applications and instead focuses on computational methods
- Discusses optimization problems and machine learning problems
Part of the book series: Natural Computing Series (NCS)
Access this book
Tax calculation will be finalised at checkout
Other ways to access
Table of contents (12 chapters)
-
Front Matter
-
Computational Intelligence for Optimization
-
Front Matter
-
-
Back Matter
Keywords
About this book
This textbook provides the reader with an essential understanding of computational methods for intelligent systems. These are defined as systems that can solve problems autonomously, in particular problems where algorithmic solutions are inconceivable for humans or not practically executable by computers. Despite the rapidly growing applications in this field, the book avoids application details, instead focusing on computational methods that equip the reader with the methodological tools and competencies necessary to tackle current and future complex applications.
The book consists of two parts: computational intelligence methods for optimization, and machine learning. Part I begins with the concept of optimization, and introduces local search algorithms, genetic algorithms, and particle swarm optimization. Part II begins with an introduction to machine learning and covers several methods, many of which can be used as supervised learning algorithms, such as decision treelearning, artificial neural networks, genetic programming, Bayesian learning, support vector machines, and ensemble methods, plus a discussion of unsupervised learning. This textbook is written in a self-contained style, suitable for undergraduate or graduate students in computer science and engineering, and for self-study by researchers and practitioners.Authors and Affiliations
About the authors
Sara Silva is a Principal Investigator at the Computer Science and Engineering Research Centre (LASIGE) of the Universidade de Lisboa, Portugal. Her main research interests are machine learning and evolutionary computation, including interdisciplinary applications in the areas of remote sensing and bioinformatics. She is the author of around 100 peer-reviewed publications, having received more than 10 nominations and awards for best paper and best researcher. In 2018 she received the Evo* Award for Outstanding Contribution to Evolutionary Computation in Europe. She created the MATLAB Genetic Programming Toolbox (GPLAB).
Bibliographic Information
Book Title: Lectures on Intelligent Systems
Authors: Leonardo Vanneschi, Sara Silva
Series Title: Natural Computing Series
DOI: https://doi.org/10.1007/978-3-031-17922-8
Publisher: Springer Cham
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: Springer Nature Switzerland AG 2023
Hardcover ISBN: 978-3-031-17921-1Published: 14 January 2023
Softcover ISBN: 978-3-031-17924-2Published: 14 January 2024
eBook ISBN: 978-3-031-17922-8Published: 13 January 2023
Series ISSN: 1619-7127
Series E-ISSN: 2627-6461
Edition Number: 1
Number of Pages: XIV, 349
Number of Illustrations: 53 b/w illustrations, 36 illustrations in colour
Topics: Artificial Intelligence