Leonardo Vanneschi,Sara Silva
Lectures on Intelligent Systems
Lectures on Intelligent Systems
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- More about Lectures on Intelligent Systems
The textbook provides an essential understanding of computational methods for intelligent systems, focusing on optimization and machine learning, with two parts covering local search algorithms, genetic algorithms, particle swarm optimization, decision tree learning, artificial neural networks, genetic programming, Bayesian learning, support vector machines, and ensemble methods. It is suitable for undergraduate or graduate students in computer science and engineering, as well as for self-study by researchers and practitioners.
Format: Paperback / softback
Length: 349 pages
Publication date: 14 January 2024
Publisher: Springer International Publishing AG
Computational Methods for Intelligent Systems
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.
Dimension: 235 x 155 (mm)
ISBN-13: 9783031179242
Edition number: 1st ed. 2023
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