Overview
- 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
Part of the book series: Machine Learning: Foundations, Methodologies, and Applications (MLFMA)
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About this book
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.
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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Keywords
Table of contents (10 chapters)
Reviews
“The book under review matured from lecture notes … . The structure and style strongly recommend the book as a thorough entry point for studying ML; the exercises and the references concluding each chapter reinforce concepts and also provide a curated guidance for further steps. … The (text)book reaches a balance between mathematical details, overview of algorithms and examples, making it suitable for a wide range of readers, and further underlining the interdisciplinary character of machine learning.” (Irina Ioana Mohorianu, zbMATH 1530.68002, 2024)
Authors and Affiliations
About the author
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.
Bibliographic Information
Book Title: Machine Learning
Book Subtitle: The Basics
Authors: Alexander Jung
Series Title: Machine Learning: Foundations, Methodologies, and Applications
DOI: https://doi.org/10.1007/978-981-16-8193-6
Publisher: Springer Singapore
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022
Hardcover ISBN: 978-981-16-8192-9Published: 22 January 2022
Softcover ISBN: 978-981-16-8195-0Published: 23 January 2023
eBook ISBN: 978-981-16-8193-6Published: 21 January 2022
Series ISSN: 2730-9908
Series E-ISSN: 2730-9916
Edition Number: 1
Number of Pages: XVII, 212
Number of Illustrations: 35 b/w illustrations, 42 illustrations in colour
Topics: Machine Learning, Data Structures and Information Theory, Artificial Intelligence, Theory of Computation, Data Mining and Knowledge Discovery