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| fixed length control field |
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| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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| fixed length control field |
220609b xxu||||| |||| 00| 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| International Standard Book Number |
9789811681929 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER |
|---|
| Classification number |
006.31 |
| Item number |
JUN |
| 100 ## - MAIN ENTRY--PERSONAL NAME |
|---|
| Personal name |
Jung, Alexander |
| 245 ## - TITLE STATEMENT |
|---|
| Title |
Machine learning : the basics |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) |
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| Name of publisher, distributor, etc |
Springer, |
| Date of publication, distribution, etc |
2022 |
| Place of publication, distribution, etc |
Singapore : |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
xvii, 212 p. ; |
| Other physical details |
ill., |
| Dimensions |
24 cm |
| 365 ## - TRADE PRICE |
|---|
| Price amount |
59.99 |
| Price type code |
EUR |
| Unit of pricing |
86.00 |
| 490 ## - SERIES STATEMENT |
|---|
| Series statement |
Machine learning: foundations, methodologies, and applications |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE |
|---|
| Bibliography, etc |
Includes bibliographical references and index. |
| 520 ## - SUMMARY, ETC. |
|---|
| Summary, etc |
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. |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name as entry element |
Machine Learning |
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| Topical term or geographic name as entry element |
Foundation |
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| Topical term or geographic name as entry element |
Methodologies |
|
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| Topical term or geographic name as entry element |
Autoencoder |
|
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| Topical term or geographic name as entry element |
Black box |
|
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| Topical term or geographic name as entry element |
Core node |
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| Topical term or geographic name as entry element |
Cluster |
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| Topical term or geographic name as entry element |
Data augmentation |
|
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| Topical term or geographic name as entry element |
DBSCAN |
|
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| Topical term or geographic name as entry element |
Empirical risk |
|
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| Topical term or geographic name as entry element |
Feature map |
|
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| Topical term or geographic name as entry element |
Gaussian mixture model(GMM) |
|
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| Topical term or geographic name as entry element |
Iterative methods |
|
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| Topical term or geographic name as entry element |
Linear span |
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| Topical term or geographic name as entry element |
Model agnostic |
|
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| Topical term or geographic name as entry element |
Ridge regression |
|
|---|
| Topical term or geographic name as entry element |
Sigmoid function |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
|---|
| Source of classification or shelving scheme |
|
| Item type |
Books |