Machine Learning The Basics /
Jung, Alexander.
Machine Learning The Basics / [electronic resource] : by Alexander Jung. - 1st ed. 2022. - XVII, 212 p. 77 illus., 42 illus. in color. online resource. - Machine Learning: Foundations, Methodologies, and Applications, 2730-9916 . - Machine Learning: Foundations, Methodologies, and Applications, .
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.
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. .
9789811681936
10.1007/978-981-16-8193-6 doi
Machine learning.
Artificial intelligence--Data processing.
Artificial intelligence.
Computer science.
Data mining.
Machine Learning.
Data Science.
Artificial Intelligence.
Models of Computation.
Data Mining and Knowledge Discovery.
Q325.5-.7
006.31
Machine Learning The Basics / [electronic resource] : by Alexander Jung. - 1st ed. 2022. - XVII, 212 p. 77 illus., 42 illus. in color. online resource. - Machine Learning: Foundations, Methodologies, and Applications, 2730-9916 . - Machine Learning: Foundations, Methodologies, and Applications, .
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.
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. .
9789811681936
10.1007/978-981-16-8193-6 doi
Machine learning.
Artificial intelligence--Data processing.
Artificial intelligence.
Computer science.
Data mining.
Machine Learning.
Data Science.
Artificial Intelligence.
Models of Computation.
Data Mining and Knowledge Discovery.
Q325.5-.7
006.31