Information-Driven Machine Learning (Registro nro. 273711)

MARC details
000 -LIDER
fixed length control field 05448nam a22005895i 4500
001 - CONTROL NUMBER
control field 978-3-031-39477-5
003 - CONTROL NUMBER IDENTIFIER
control field DE-He213
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20250516155934.0
007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION
fixed length control field cr nn 008mamaa
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fixed length control field 231201s2024 sz | s |||| 0|eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9783031394775
-- 978-3-031-39477-5
050 #4 - LIBRARY OF CONGRESS CALL NUMBER
Classification number Q336
072 #7 - SUBJECT CATEGORY CODE
Subject category code UN
Source bicssc
072 #7 - SUBJECT CATEGORY CODE
Subject category code COM021000
Source bisacsh
072 #7 - SUBJECT CATEGORY CODE
Subject category code UN
Source thema
082 04 - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 005.7
Edition number 23
100 1# - MAIN ENTRY--PERSONAL NAME
Personal name Friedland, Gerald.
Relator term author.
Relator code aut
-- http://id.loc.gov/vocabulary/relators/aut
245 10 - TITLE STATEMENT
Title Information-Driven Machine Learning
Medium [electronic resource] :
Remainder of title Data Science as an Engineering Discipline /
Statement of responsibility, etc. by Gerald Friedland.
250 ## - EDITION STATEMENT
Edition statement 1st ed. 2024.
264 #1 -
-- Cham :
-- Springer International Publishing :
-- Imprint: Springer,
-- 2024.
300 ## - PHYSICAL DESCRIPTION
Extent XXII, 267 p. 50 illus., 33 illus. in color.
Other physical details online resource.
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-- rdamedia
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-- online resource
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347 ## -
-- text file
-- PDF
-- rda
505 0# - FORMATTED CONTENTS NOTE
Formatted contents note Preface -- 1 Introduction -- 2 The Automated Scientific Process -- 3 The (Black Box) Machine Learning Process -- 4 Information Theory -- 5 Capacity -- 6 The Mechanics of Generalization -- 7 Meta-Math: Exploring the Limits of Modeling -- 8 Capacity of Neural Networks -- 8 Capacity of Neural Networks -- 10 Capacities of some other Machine Learning Methods -- 11 Data Collection and Preparation -- 12 Measuring Data Sufficiency -- 13 Machine Learning Operations -- 14 Explainability -- 15 Repeatability and Reproducibility -- 16 The Curse of Training and the Blessing of High Dimensionality -- 16 The Curse of Training and the Blessing of High Dimensionality -- Appendix A Recap: The Logarithm -- Appendix B More on Complexity -- Appendix C Concepts Cheat Sheet -- Appendix D A Review Form that Promotes Reproducibility -- List of Illustrations -- Bibliography.
520 ## - SUMMARY, ETC.
Summary, etc. This groundbreaking book transcends traditional machine learning approaches by introducing information measurement methodologies that revolutionize the field. Stemming from a UC Berkeley seminar on experimental design for machine learning tasks, these techniques aim to overcome the 'black box' approach of machine learning by reducing conjectures such as magic numbers (hyper-parameters) or model-type bias. Information-based machine learning enables data quality measurements, a priori task complexity estimations, and reproducible design of data science experiments. The benefits include significant size reduction, increased explainability, and enhanced resilience of models, all contributing to advancing the discipline's robustness and credibility. While bridging the gap between machine learning and disciplines such as physics, information theory, and computer engineering, this textbook maintains an accessible and comprehensive style, making complex topics digestible for abroad readership. Information-Driven Machine Learning explores the synergistic harmony among these disciplines to enhance our understanding of data science modeling. Instead of solely focusing on the "how," this text provides answers to the "why" questions that permeate the field, shedding light on the underlying principles of machine learning processes and their practical implications. By advocating for systematic methodologies grounded in fundamental principles, this book challenges industry practices that have often evolved from ideologic or profit-driven motivations. It addresses a range of topics, including deep learning, data drift, and MLOps, using fundamental principles such as entropy, capacity, and high dimensionality. Ideal for both academia and industry professionals, this textbook serves as a valuable tool for those seeking to deepen their understanding of data science as an engineering discipline. Its thought-provoking content stimulates intellectual curiosity and caters to readers who desire more than just code or ready-made formulas. The text invites readers to explore beyond conventional viewpoints, offering an alternative perspective that promotes a big-picture view for integrating theory with practice. Suitable for upper undergraduate or graduate-level courses, this book can also benefit practicing engineers and scientists in various disciplines by enhancing their understanding of modeling and improving data measurement effectively.
541 ## - IMMEDIATE SOURCE OF ACQUISITION NOTE
Owner UABC ;
Method of acquisition Perpetuidad
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Término temático o nombre geográfico como elemento de entrada Artificial intelligence
Subdivisión general Data processing.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Término temático o nombre geográfico como elemento de entrada Machine learning.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Término temático o nombre geográfico como elemento de entrada Data structures (Computer science).
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Término temático o nombre geográfico como elemento de entrada Information theory.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Término temático o nombre geográfico como elemento de entrada Expert systems (Computer science).
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Término temático o nombre geográfico como elemento de entrada Artificial intelligence.
650 14 - SUBJECT ADDED ENTRY--TOPICAL TERM
Término temático o nombre geográfico como elemento de entrada Data Science.
650 24 - SUBJECT ADDED ENTRY--TOPICAL TERM
Término temático o nombre geográfico como elemento de entrada Machine Learning.
650 24 - SUBJECT ADDED ENTRY--TOPICAL TERM
Término temático o nombre geográfico como elemento de entrada Data Structures and Information Theory.
650 24 - SUBJECT ADDED ENTRY--TOPICAL TERM
Término temático o nombre geográfico como elemento de entrada Knowledge Based Systems.
650 24 - SUBJECT ADDED ENTRY--TOPICAL TERM
Término temático o nombre geográfico como elemento de entrada Artificial Intelligence.
710 2# - ADDED ENTRY--CORPORATE NAME
Corporate name or jurisdiction name as entry element SpringerLink (Online service)
773 0# - HOST ITEM ENTRY
Title Springer Nature eBook
776 08 - ADDITIONAL PHYSICAL FORM ENTRY
Relationship information Printed edition:
International Standard Book Number 9783031394768
776 08 - ADDITIONAL PHYSICAL FORM ENTRY
Relationship information Printed edition:
International Standard Book Number 9783031394782
776 08 - ADDITIONAL PHYSICAL FORM ENTRY
Relationship information Printed edition:
International Standard Book Number 9783031394799
856 40 - ELECTRONIC LOCATION AND ACCESS
Public note Libro electrónico
Uniform Resource Identifier http://libcon.rec.uabc.mx:2048/login?url=https://doi.org/10.1007/978-3-031-39477-5
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942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type Libro Electrónico
Existencias
Estado de retiro Colección Ubicación permanente Ubicación actual Fecha de ingreso Total Checkouts Date last seen Número de copia Tipo de material
  Colección de Libros Electrónicos Biblioteca Electrónica Biblioteca Electrónica 16/05/2025   16/05/2025 1 Libro Electrónico

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