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Lectures on intelligent systems

Record Type:	Electronic resources : Monograph/item
Title/Author:	Lectures on intelligent systemsby Leonardo Vanneschi, Sara Silva.
Author:	Vanneschi, Leonardo.
other author:	Silva, Sara.
Published:	Cham :Springer International Publishing :2023.
Description:	xiv, 349 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Artificial intelligenceMathematics.
Online resource:	https://doi.org/10.1007/978-3-031-17922-8
ISBN:	9783031179228$q(electronic bk.)

Lectures on intelligent systems
Vanneschi, Leonardo.

Lectures on intelligent systems[electronic resource] /by Leonardo Vanneschi, Sara Silva. - Cham :Springer International Publishing :2023. - xiv, 349 p. :ill. (some col.), digital ;24 cm. - Natural computing series. - Natural computing series..

Chapter 1: Introduction -- Chapter 2: Optimization Problems and Local Search -- Chapter 3: Genetic Algorithms -- Chapter 4: Particle Swarm Optimization -- Chapter 5: Introduction to Machine Learning -- Chapter 6: Decision Tree Learning -- Chapter 7: Artificial Neural Networks -- Chapter 8: Genetic Programming -- Bayesian Learning -- Chapter 10: Support Vector Machines -- Chapter 11: Ensemble Methods -- Chapter 12: Unsupervised Learning.

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 tree learning, 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.

ISBN: 9783031179228$q(electronic bk.)

Standard No.: 10.1007/978-3-031-17922-8doiSubjects--Topical Terms:

324997
Artificial intelligence
--Mathematics.

LC Class. No.: Q335

Dewey Class. No.: 006.3

Lectures on intelligent systems
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505 0 $a Chapter 1: Introduction -- Chapter 2: Optimization Problems and Local Search -- Chapter 3: Genetic Algorithms -- Chapter 4: Particle Swarm Optimization -- Chapter 5: Introduction to Machine Learning -- Chapter 6: Decision Tree Learning -- Chapter 7: Artificial Neural Networks -- Chapter 8: Genetic Programming -- Bayesian Learning -- Chapter 10: Support Vector Machines -- Chapter 11: Ensemble Methods -- Chapter 12: Unsupervised Learning.
520 $a 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 tree learning, 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.
650 0 $a Artificial intelligence $x Mathematics. $3 324997
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