03749nam 22006135 450 991073409280332120250204112212.09783031179228303117922610.1007/978-3-031-17922-8(MiAaPQ)EBC7176850(Au-PeEL)EBL7176850(CKB)26015349500041(DE-He213)978-3-031-17922-8(PPN)267807503(EXLCZ)992601534950004120230113d2023 u| 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierLectures on Intelligent Systems /by Leonardo Vanneschi, Sara Silva1st ed. 2023.Cham :Springer International Publishing :Imprint: Springer,2023.1 online resource (352 pages)Natural Computing Series,2627-6461Print version: Vanneschi, Leonardo Lectures on Intelligent Systems Cham : Springer International Publishing AG,c2023 9783031179211 Includes bibliographical references.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.Natural Computing Series,2627-6461Artificial intelligenceArtificial IntelligenceIntel·ligència artificialthubIntel·ligència computacionalthubMatemàticathubLlibres electrònicsthubArtificial intelligence.Artificial Intelligence.Intel·ligència artificial.Intel·ligència computacionalMatemàtica060006.3Vanneschi Leonardo1353935Silva SaraMiAaPQMiAaPQMiAaPQBOOK9910734092803321Lectures on intelligent systems3400398UNINA