Lectures on Intelligent Systems / by Leonardo Vanneschi, Sara Silva.

Format:

Book

Author/Creator:

Vanneschi, Leonardo, Author.

Silva, Sara, Author.

Contributor:

SpringerLink (Online service)

Series:

Computer Science (SpringerNature-11645)

Natural computing series

Natural Computing Series

Language:

English

Subjects (All):

Artificial intelligence.

Artificial Intelligence.

Local Subjects:

Artificial Intelligence.

Physical Description:

1 online resource (XIV, 349 pages) : 89 illustrations, 36 illustrations in color.

Edition:

1st ed. 2023.

Contained In:

Springer Nature eBook

Place of Publication:

Cham : Springer International Publishing : Imprint: Springer, 2023.

System Details:

text file PDF

Summary:

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.

Contents:

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.

Other Format:

Printed edition:

ISBN:

978-3-031-17922-8

9783031179228

Access Restriction:

Restricted for use by site license.