Machine Learning: The Basics (Machine Learning: Foundations, Methodologies, and Applications) - Softcover

Buch 2 von 4: Machine Learning: Foundations, Methodologies, and Applications

Jung, Alexander

 
9789811681950: Machine Learning: The Basics (Machine Learning: Foundations, Methodologies, and Applications)
Softcover
ISBN 10: 9811681953 ISBN 13: 9789811681950
Verlag: Springer, 2023

Inhaltsangabe

Machine learning (ML) has become a commonplace element in our everyday lives and a standard tool for many fields of science and engineering. To make optimal use of ML, it is essential to understand its underlying principles. 

This book approaches ML as the computational implementation of the scientific principle. This principle consists of continuously adapting a model of a given data-generating phenomenon by minimizing some form of loss incurred by its predictions. 

The book trains readers to break down various ML applications and methods in terms of data, model, and loss, thus helping them to choose from the vast range of ready-made ML methods.

The book’s three-component approach to ML provides uniform coverage of a wide range of concepts and techniques. As a case in point, techniques for regularization, privacy-preservation as well as explainability amount tospecific design choices for the model, data, and loss of a ML method. 

Die Inhaltsangabe kann sich auf eine andere Ausgabe dieses Titels beziehen.

Über die Autorin bzw. den Autor

Alexander Jung is Assistant Professor of Machine Learning at the Department of Computer Science, Aalto University where he leads the research group "Machine Learning for Big Data". His courses on machine learning, artificial intelligence, and convex optimization are among the most popular courses offered at Aalto University. He received a Best Student Paper Award at the premium signal processing conference IEEE ICASSP in 2011, an Amazon Web Services Machine Learning Award in 2018, and was elected as Teacher of the Year by the Department of Computer Science in 2018. He serves as an Associate Editor for the IEEE Signal Processing Letters.  

Von der hinteren Coverseite

Machine learning (ML) has become a commonplace element in our everyday lives and a standard tool for many fields of science and engineering. To make optimal use of ML, it is essential to understand its underlying principles. 

This book approaches ML as the computational implementation of the scientific principle. This principle consists of continuously adapting a model of a given data-generating phenomenon by minimizing some form of loss incurred by its predictions. 

The book trains readers to break down various ML applications and methods in terms of data, model, and loss, thus helping them to choose from the vast range of ready-made ML methods.

The book’s three-component approach to ML provides uniform coverage of a wide range of concepts and techniques. As a case in point, techniques for regularization, privacy-preservation as well as explainability amount tospecific design choices for the model, data, and loss of a ML method. 

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Verlag
Springer
Erscheinungsdatum
2023
Sprache
Englisch
ISBN 10 
9811681953
ISBN 13 
9789811681950
Einband
Taschenbuch
Auflage
1
Anzahl der Seiten
232
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9789811681929: Machine Learning: The Basics (Machine Learning: Foundations, Methodologies, and Applications)

Vorgestellte Ausgabe

ISBN 10:  9811681929 ISBN 13:  9789811681929
Verlag: Springer, 2022
Hardcover