Machine Learning_ The Basics (Machine Learning_ Foundations, Methodologies, and Applications)_Alexander Jung.pdf

Machine Learning Alexander Jung Machine Learning: Foundations, Methodologies, and Applications The Basics Machine Learning: Foundations, Methodologies, and Applications Series Editors Kay Chen Tan, Department of Computing, Hong Kong Polytechnic University, Hong Kong, China Dacheng Tao, University of Technology, Sydney, Australia Books published in this series focus on the theory and computational foundations, advanced methodologies and practical applications of machine learning, ideally combining mathematically rigorous treatments of a contemporary topics in machine learning with specific illustrations in relevant algorithm designs and demonstrations in real-world applications. The intended readership includes research students and researchers in computer science, computer engineering, electrical engineering, data science, and related areas seeking a convenient medium to track the progresses made in the foundations, methodologies, and applications of machine learning. Topics considered include all areas of machine learning, including but not limited to: •Decision tree •Artificial neural networks •Kernel learning •Bayesian learning •Ensemble methods •Dimension reduction and metric learning •Reinforcement learning •Meta learning and learning to learn •Imitation learning •Computational learning theory •Probabilistic graphical models •Transfer learning •Multi-view and multi-task learning •Graph neural networks •Generative adversarial networks •Federated learning This series includes monographs, introductory and advanced textbooks, and state- of-the-art collections. Furthermore, it supports Open Access publication mode. More information about this series athttps://link.springer.com/bookseries/16715 Alexander Jung MachineLearning The Basics Alexander Jung Department of Computer Science Aalto University Espoo, Finland ISSN 2730-9908 ISSN 2730-9916 (electronic) Machine Learning: Foundations, Methodologies, and Applications ISBN 978-981-16-8192-9 ISBN 978-981-16-8193-6 (