Machine Learning: The Basics
AUTHOR | Jung, Alexander |
PUBLISHER | Springer (01/22/2022) |
PRODUCT TYPE | Hardcover (Hardcover) |
Description
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.
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Product Format
Product Details
ISBN-13:
9789811681929
ISBN-10:
9811681929
Binding:
Hardback or Cased Book (Sewn)
Content Language:
English
More Product Details
Page Count:
212
Carton Quantity:
22
Product Dimensions:
6.14 x 0.56 x 9.21 inches
Weight:
1.11 pound(s)
Feature Codes:
Illustrated
Country of Origin:
NL
Subject Information
BISAC Categories
Computers | Artificial Intelligence - General
Computers | Information Theory
Computers | Computer Science
Descriptions, Reviews, Etc.
jacket back
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.
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.
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publisher marketing
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.
Show More
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