TY  - BOOK
AU  - Jung,Alexander
TI  - Machine Learning: The Basics 
T2  - Machine Learning: Foundations, Methodologies, and Applications,
SN  - 9789811681936
U1  - 006.31 23
PY  - 2022///
CY  - Singapore
PB  - Springer Nature Singapore, Imprint: Springer
KW  - Machine learning
KW  - Artificial intelligence-Data processing
KW  - Artificial intelligence
KW  - Computer science
KW  - Data mining
KW  - Machine Learning
KW  - Data Science
KW  - Artificial Intelligence
KW  - Models of Computation
KW  - Data Mining and Knowledge Discovery
N1  - 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
N2  - 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.
UR  - https://doi.org/10.1007/978-981-16-8193-6
ER  -