Machine Learning - The Basics (Hardcover, 1st ed. 2022)

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.

General

Imprint:	Springer Verlag, Singapore
Country of origin:	Singapore
Series:	Machine Learning: Foundations, Methodologies, and Applications
Release date:	2022
First published:	2022
Authors:	Alexander Jung
Dimensions:	235 x 155 x 27mm (L x W x T)
Format:	Hardcover
Pages:	212
Edition:	1st ed. 2022
ISBN-13:	978-981-16-8192-9
Categories:	Books > Computing & IT > General theory of computing > Data structures Books > Computing & IT > Computer programming > Algorithms & procedures Books > Computing & IT > Applications of computing > Databases > Data mining Books > Computing & IT > Applications of computing > Artificial intelligence > Machine learning
LSN:	981-16-8192-9
Barcode:	9789811681929

Is the information for this product incomplete, wrong or inappropriate? Let us know about it.

Does this product have an incorrect or missing image? Send us a new image.

Is this product missing categories? Add more categories.

Review This Product

No reviews yet - be the first to create one!