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Machine Learning: The Basics by Alexander Jung: New
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Specificaties
- Objectstaat
- Book Title
- Machine Learning: The Basics
- Publication Date
- 2022-01-22
- ISBN
- 9789811681929
Over dit product
Product Identifiers
Publisher
Springer
ISBN-10
9811681929
ISBN-13
9789811681929
eBay Product ID (ePID)
9057254748
Product Key Features
Number of Pages
Xvii, 212 Pages
Language
English
Publication Name
Machine Learning-The Basics
Publication Year
2022
Subject
Intelligence (Ai) & Semantics, Probability & Statistics / General, General, Databases / General
Type
Textbook
Subject Area
Mathematics, Computers, Science
Series
Machine Learning: Foundations, Methodologies, and Applications Ser.
Format
Hardcover
Dimensions
Item Weight
18.3 Oz
Item Length
9.3 in
Item Width
6.1 in
Additional Product Features
Dewey Edition
23
Number of Volumes
1 vol.
Illustrated
Yes
Dewey Decimal
006.31
Table Of Content
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.
Synopsis
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., 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.
LC Classification Number
Q325.5-.7
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