000			01530 a2200205 4500
003			OSt
005			20250910171528.0
008			250910b |||||||| |||| 00| 0 eng d
020			_a9789811681929
082			_a006.31 _bJ951m
100			_aJung, Alexander
245			_aMachine learning _bThe basics _cAlexander Jung
260			_bSpringer _c2022 _aSingapore
300			_axvii, 212p
440			_aMachine learning : foundations, methodologies, and applications
490			_a/ edited by Kay Chen Tan and Dacheng Tao
520			_aMachine 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.
650			_aMachine learning
942			_cBK
999			_c567634 _d567634