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Titlebook: Machine Learning; The Basics Alexander Jung Textbook 2022 The Editor(s) (if applicable) and The Author(s), under exclusive license to Sprin

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发表于 2025-3-21 18:53:46 | 显示全部楼层 |阅读模式
书目名称Machine Learning
副标题The Basics
编辑Alexander Jung
视频video
概述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
丛书名称Machine Learning: Foundations, Methodologies, and Applications
图书封面Titlebook: Machine Learning; The Basics Alexander Jung Textbook 2022 The Editor(s) (if applicable) and The Author(s), under exclusive license to Sprin
描述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. .
出版日期Textbook 2022
关键词Machine Learning; Modelling; Artificial Intelligence; Deep Learning; Optimization; Data Analysis; Signal P
版次1
doihttps://doi.org/10.1007/978-981-16-8193-6
isbn_softcover978-981-16-8195-0
isbn_ebook978-981-16-8193-6Series ISSN 2730-9908 Series E-ISSN 2730-9916
issn_series 2730-9908
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapor
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发表于 2025-3-21 21:32:19 | 显示全部楼层
Alexander Jungegungen waren das . und das . Ist nun eine Raumkurve . gegeben, so kann man ebenso die möglichen Bewegungen einer Geraden t betrachten, bei denen sie stets Tangente von . bleibt. Durch diese Bedingung allein ist indes eine solche Bewegung der Geraden im Raum noch nicht eindeutig bestimmt, weil sie s
发表于 2025-3-22 01:31:28 | 显示全部楼层
Alexander JungDas der geographischen Ortsbestimmung dienende Gradnetz der Erde geht dabei in das Gradnetz des Globus über, das einerseits aus den Großkreisen besteht, die durch zwei diametral gegenüberliegende Punkte, den . und den ., gehen und . heißen, und anderseits aus den Parallelkreisen, die die Meridiane r
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Introduction, choose the right gear (clothing, wax) it is vital to have some idea for the maximum daytime temperature which is typically reached around early afternoon. If we expect a maximum daytime temperature of around plus 5 degrees, we might not put on the extra warm jacket but rather take only some extra s
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Model Validation and Selection,s . that incurs minimum average loss on some labeled data points that serve as the .. We refer to the average loss incurred by a hypothesis on the training set as the training error. The minimum average loss achieved by a hypothesis that solves the ERM might be referred to as the training error of t
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Feature Learning,urally from the available hard and software. For example, we might use the numeric measurement . delivered by a sensing device as a feature. However, we could augment this single feature with new features such as the powers . and . or adding a constant .. Each of these computations produces a new fe
发表于 2025-3-23 08:13:02 | 显示全部楼层
Transparent and Explainable ML,rent (or explainable) as explainable ML. Providing explanations for the predictions of a ML method is particulary important when these predictions inform decision making [.]. Explanations for automated decision making system have become a legal requirement [.].
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