Information-Driven Machine Learning (Record no. 87068)
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| fixed length control field | 05424nam a22005775i 4500 |
| 001 - CONTROL NUMBER | |
| control field | 978-3-031-39477-5 |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20240730170609.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 231201s2024 sz | s |||| 0|eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| ISBN | 9783031394775 |
| -- | 978-3-031-39477-5 |
| 082 04 - CLASSIFICATION NUMBER | |
| Call Number | 005.7 |
| 100 1# - AUTHOR NAME | |
| Author | Friedland, Gerald. |
| 245 10 - TITLE STATEMENT | |
| Title | Information-Driven Machine Learning |
| Sub Title | Data Science as an Engineering Discipline / |
| 250 ## - EDITION STATEMENT | |
| Edition statement | 1st ed. 2024. |
| 300 ## - PHYSICAL DESCRIPTION | |
| Number of Pages | XXII, 267 p. 50 illus., 33 illus. in color. |
| 505 0# - FORMATTED CONTENTS NOTE | |
| Remark 2 | 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. |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| General subdivision | Data processing. |
| 856 40 - ELECTRONIC LOCATION AND ACCESS | |
| Uniform Resource Identifier | https://doi.org/10.1007/978-3-031-39477-5 |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Koha item type | eBooks |
| 264 #1 - | |
| -- | Cham : |
| -- | Springer International Publishing : |
| -- | Imprint: Springer, |
| -- | 2024. |
| 336 ## - | |
| -- | text |
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| -- | computer |
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| -- | online resource |
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| 347 ## - | |
| -- | text file |
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| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Artificial intelligence |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Machine learning. |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Data structures (Computer science). |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Information theory. |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Expert systems (Computer science). |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Artificial intelligence. |
| 650 14 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Data Science. |
| 650 24 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Machine Learning. |
| 650 24 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Data Structures and Information Theory. |
| 650 24 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Knowledge Based Systems. |
| 650 24 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Artificial Intelligence. |
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