Details
Machine Learning and Metaheuristic Computation
1. Aufl.
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115,99 € |
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| Verlag: | Wiley |
| Format: | |
| Veröffentl.: | 01.11.2024 |
| ISBN/EAN: | 9781394229673 |
| Sprache: | englisch |
| Anzahl Seiten: | 432 |
DRM-geschütztes eBook, Sie benötigen z.B. Adobe Digital Editions und eine Adobe ID zum Lesen.
Beschreibungen
<p><b>Learn to bridge the gap between machine learning and metaheuristic methods to solve problems in optimization approaches</b> <p>Few areas of technology have greater potential to revolutionize the globe than artificial intelligence. Two key areas of artificial intelligence, machine learning and metaheuristic computation, have an enormous range of individual and combined applications in computer science and technology. To date, these two complementary paradigms have not always been treated together, despite the potential of a combined approach which maximizes the utility and minimizes the drawbacks of both. <p><i>Machine Learning and Metaheuristic Computation</i> offers an introduction to both of these approaches and their joint applications. Both a reference text and a course, it is built around the popular Python programming language to maximize utility. It guides the reader gradually from an initial understanding of these crucial methods to an advanced understanding of cutting-edge artificial intelligence tools. <p>The text also provides: <ul><li>Treatment suitable for readers with only basic mathematical training</li><li>Detailed discussion of topics including dimensionality reduction, clustering methods, differential evolution, and more</li><li>A rigorous but accessible vision of machine learning algorithms and the most popular approaches of metaheuristic optimization</li></ul> <p><i>Machine Learning and Metaheuristic Computation</i> is ideal for students, researchers, and professionals looking to combine these vital methods to solve problems in optimization approaches.
<p><b>Erik Cuevas, PhD,</b> is a Full Professor in the Department of Electronics at the University of Guadalajara. He is a Member of the Mexican Academy of Sciences and the National System of Researchers. He has provided editorial services on several specialized journals. <p><b>Jorge Galvez, PhD,</b> is a Full Professor in the Department of Innovation Based on Information and Knowledge at the University of Guadalajara. He is a Member of the Mexican Academy of Sciences and the National System of Researchers. <p><b>Omar Avalos, PhD,</b> is a Professor in the Electronics and Computing Division of the University Center for Exact Sciences and Engineering at the University of Guadalajara. He is a Member of the Mexican Academy of Sciences and the National System of Researchers. <p><b>Fernando Wario, PhD,</b> is a Professor at the University of Guadalajara and an Associate Researcher at the Institute of Cognitive Sciences and Technologies (ISTC) in Rome, Italy. He is a Member of the Mexican Academy of Sciences and the National System of Researchers.
<p><b>Learn to bridge the gap between machine learning and metaheuristic methods to solve problems in optimization approaches</b> <p>Few areas of technology have greater potential to revolutionize the globe than artificial intelligence. Two key areas of artificial intelligence, machine learning and metaheuristic computation, have an enormous range of individual and combined applications in computer science and technology. To date, these two complementary paradigms have not always been treated together, despite the potential of a combined approach which maximizes the utility and minimizes the drawbacks of both. <p><i>Machine Learning and Metaheuristic Computation</i> offers an introduction to both of these approaches and their joint applications. Both a reference text and a course, it is built around the popular Python programming language to maximize utility. It guides the reader gradually from an initial understanding of these crucial methods to an advanced understanding of cutting-edge artificial intelligence tools. <p>The text also provides: <ul><li>Treatment suitable for readers with only basic mathematical training</li><li>Detailed discussion of topics including dimensionality reduction, clustering methods, differential evolution, and more</li><li>A rigorous but accessible vision of machine learning algorithms and the most popular approaches of metaheuristic optimization</li></ul> <p><i>Machine Learning and Metaheuristic Computation</i> is ideal for students, researchers, and professionals looking to combine these vital methods to solve problems in optimization approaches.
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