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Machine Learning : The Basics / by Alexander Jung.
- Format:
- Book
- Author/Creator:
- Jung, Alexander., Author.
- Series:
- Computer Science (SpringerNature-11645)
- Machine Learning: Foundations, Methodologies, and Applications, 2730-9916
- Language:
- English
- Subjects (All):
- 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.
- Local Subjects:
- Machine Learning.
- Data Science.
- Artificial Intelligence.
- Models of Computation.
- Data Mining and Knowledge Discovery.
- Physical Description:
- 1 online resource (XVII, 212 pages) : 77 illustrations, 42 illustrations in color.
- Edition:
- 1st ed. 2022.
- Contained In:
- Springer Nature eBook
- Place of Publication:
- Singapore : Springer Nature Singapore : Imprint: Springer, 2022.
- System Details:
- text file PDF
- Summary:
- 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. .
- Contents:
- 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.
- Other Format:
- Printed edition:
- ISBN:
- 978-981-16-8193-6
- 9789811681936
- Access Restriction:
- Restricted for use by site license.
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