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Machine learning = the basics /

Jung, Alexander.

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Machine learning = the basics /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Machine learning/ by Alexander Jung.
其他題名:	the basics /
作者:	Jung, Alexander.
出版者:	Singapore :Springer Singapore : : 2022.,
面頁冊數:	xvii, 212 p. :ill. (some col.), digital ;24 cm.
內容註:	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.
Contained By:	Springer Nature eBook
標題:	Machine learning. -
電子資源:	https://doi.org/10.1007/978-981-16-8193-6
ISBN:	9789811681936

Machine learning = the basics /
Jung, Alexander.

Machine learningthe basics /[electronic resource] :by Alexander Jung. - Singapore :Springer Singapore :2022. - xvii, 212 p. :ill. (some col.), digital ;24 cm. - Machine learning: foundations, methodologies, and applications,2730-9916. - Machine learning: foundations, methodologies, and applications..

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.

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.

ISBN: 9789811681936

Standard No.: 10.1007/978-981-16-8193-6doiSubjects--Topical Terms:

533906
Machine learning.

LC Class. No.: Q325.5 / .J86 2022

Dewey Class. No.: 006.31

Machine learning = the basics /
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