Information-Driven Machine Learning Data Science as an Engineering Discipline /

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&#...

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Detalles Bibliográficos
Autor Principal:	Friedland, Gerald (Author)
Autor Corporativo:	SpringerLink (Online service)
Formato:	Electrónico eBook
Idioma:	inglés
Publicado:	Cham : Springer International Publishing : Imprint: Springer, 2024.
Edición:	1st ed. 2024.
Subjects:	Artificial intelligence > Data processing. Machine learning. Data structures (Computer science). Information theory. Expert systems (Computer science). Artificial intelligence. Data Science. Machine Learning. Data Structures and Information Theory. Knowledge Based Systems. Artificial Intelligence.
Acceso en liña:	https://doi.org/10.1007/978-3-031-39477-5
Tags:	Engadir etiqueta Sen Etiquetas, Sexa o primeiro en etiquetar este rexistro!

Table of Contents:

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.

Information-Driven Machine Learning Data Science as an Engineering Discipline /

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