03285nam 2200493 450 99654796660331620230517010122.03-031-17922-610.1007/978-3-031-17922-8(MiAaPQ)EBC7176850(Au-PeEL)EBL7176850(CKB)26015349500041(DE-He213)978-3-031-17922-8(PPN)267807503(EXLCZ)992601534950004120230517d2023 uy 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierLectures on intelligent systems /Leonardo Vanneschi, Sara Silva1st ed. 2023.Cham, Switzerland :Springer,[2023]©20231 online resource (352 pages)Natural Computing SeriesPrint 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 SeriesArtificial intelligenceArtificial intelligence.060Vanneschi Leonardo1353935Silva SaraMiAaPQMiAaPQMiAaPQBOOK996547966603316Lectures on intelligent systems3400398UNISA