LEADER 03285nam 2200493 450 001 996547966603316 005 20230517010122.0 010 $a3-031-17922-6 024 7 $a10.1007/978-3-031-17922-8 035 $a(MiAaPQ)EBC7176850 035 $a(Au-PeEL)EBL7176850 035 $a(CKB)26015349500041 035 $a(DE-He213)978-3-031-17922-8 035 $a(PPN)267807503 035 $a(EXLCZ)9926015349500041 100 $a20230517d2023 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aLectures on intelligent systems /$fLeonardo Vanneschi, Sara Silva 205 $a1st ed. 2023. 210 1$aCham, Switzerland :$cSpringer,$d[2023] 210 4$dİ2023 215 $a1 online resource (352 pages) 225 1 $aNatural Computing Series 311 08$aPrint version: Vanneschi, Leonardo Lectures on Intelligent Systems Cham : Springer International Publishing AG,c2023 9783031179211 320 $aIncludes bibliographical references. 327 $aChapter 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. 330 $aThis textbook provides the reader with an essential understanding of computational methods for intelligent systems. These are defined as systems that can solve problems autonomously, in particular problems where algorithmic solutions are inconceivable for humans or not practically executable by computers. Despite the rapidly growing applications in this field, the book avoids application details, instead focusing on computational methods that equip the reader with the methodological tools and competencies necessary to tackle current and future complex applications. The book consists of two parts: computational intelligence methods for optimization, and machine learning. Part I begins with the concept of optimization, and introduces local search algorithms, genetic algorithms, and particle swarm optimization. Part II begins with an introduction to machine learning and covers several methods, many of which can be used as supervised learning algorithms, such as decision tree learning, artificial neural networks, genetic programming, Bayesian learning, support vector machines, and ensemble methods, plus a discussion of unsupervised learning. This textbook is written in a self-contained style, suitable for undergraduate or graduate students in computer science and engineering, and for self-study by researchers and practitioners. 410 0$aNatural Computing Series 606 $aArtificial intelligence 615 0$aArtificial intelligence. 676 $a060 700 $aVanneschi$b Leonardo$01353935 702 $aSilva$b Sara 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a996547966603316 996 $aLectures on intelligent systems$93400398 997 $aUNISA