Machine learning : the basics / Alexander Jung
By: Jung, Alexander [author].
Contributor(s): Ohio Library and Information Network.
Series: Publisher: Singapore : Springer, ©2022Copyright date: ©2022Description: 212 p: illustrations (some color).Content type: text Media type: computer Carrier type: online resourceISBN: 9789811681929.Subject(s): Machine learningGenre/Form: Print books.Current location | Call number | Status | Date due | Barcode | Item holds |
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On Shelf | Q325.5 .J86 2022 (Browse shelf) | Available | AU00000000018689 |
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Q325.5 .H39 2009 The elements of statistical learning : data mining, inference, and prediction / | Q325.5 .H83 2020 Machine Learning for Business | Q325.5 .H89 2022 Designing Machine Learning Systems | Q325.5 .J86 2022 Machine learning : the basics / | Q325.5 .K454 2019 Deep learning / | Q325.5 .K455 2015 Fundamentals of machine learning for predictive data analytics : algorithms, worked examples, and case studies / | Q325.5 .K455 2020 Fundamentals of machine learning for predictive data analytics : algorithms, worked examples, and case studies / |
Includes bibliographical references and index
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
Available to OhioLINK libraries
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 to specific design choices for the model, data, and loss of a ML method