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Springer Philosophy of Science for Machine Learning - Synthese Library 527

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Description

This open access book from Springer provides a comprehensive and systematic examination of key concepts and applications in philosophy of science as they relate to machine learning. The current discourse on epistemic and methodological challenges posed by machine learning in scientific fields remains fragmented and lacks cohesive frameworks for understanding. This volume brings together expert researchers to structure the debate in ways that enable readers to navigate this rapidly evolving field with clarity and confidence. By organising complex ideas around core issues and emerging perspectives, the book establishes common ground amongst scholars and practitioners seeking to ground machine learning approaches in rigorous philosophical foundations. Ideal for researchers, academics, and advanced students in engineering, computer science, and philosophy of science who need to understand how machine learning intersects with scientific methodology and knowledge production.

Key Features

Springer's systematic approach to philosophy of science challenges and opportunities in machine learning research and development.

Open access format from Springer ensures broad accessibility for researchers and students across institutions and geographical boundaries.

Expert contributions structure complex debates about machine learning's epistemic implications in scientific practice and theory.

Part of the Synthese Library series, positioning this work within established philosophical scholarship and academic discourse.

Provides frameworks for navigating fragmented conversations about methodology and machine learning applications in science.

Product Specifications

Format
hardcover
Domain
Amazon UK
Release Date
09 December 2025
Listed Since
16 July 2025

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