Alexander Jung: Machine Learning | Buch

Cover: 9789811681929 | Machine Learning | The Basics | Alexander Jung | Buch | xvii | 2022

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Beschreibung

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.

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.

Über 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.

Zusammenfassung

Proposes a simple three-component approach to formalizing machine learning problems and methods

Interprets typical machine learning methods using the unified scientific cycle model: forming hypothesis

Covers hot topics such as explainable and privacy-preserving machine learning

Inhaltsverzeichnis

Introduction.- Components of ML.- The Landscape of ML.- Empirical Risk Minimization.- Gradient-Based Learning.- Model Validation and Selection.- Regularization.- Clustering.- Feature Learning.- Transparant and Explainable ML.

Details

Erscheinungsjahr:	2022
Genre:	Informatik
Rubrik:	Naturwissenschaften & Technik
Medium:	Buch
Reihe:	Machine Learning: Foundations, Methodologies, and Applications
Inhalt:	xvii 212 S. 35 s/w Illustr. 42 farbige Illustr. 212 p. 77 illus. 42 illus. in color.
ISBN-13:	9789811681929
ISBN-10:	9811681929
Sprache:	Englisch
Ausstattung / Beilage:	HC runder Rücken kaschiert
Einband:	Gebunden
Autor:	Jung, Alexander
Auflage:	1st ed. 2022
Hersteller:	Springer Singapore Springer Nature Singapore Machine Learning: Foundations, Methodologies, and Applications
Maße:	241 x 160 x 19 mm
Von/Mit:	Alexander Jung
Erscheinungsdatum:	22.01.2022
Gewicht:	0,518 kg

Artikel-ID: 120726045