A dynamic interaction between machine learning and the philosophy of science

Minds and Machines 14 (4):539-549 (2004)
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Abstract

The relationship between machine learning and the philosophy of science can be classed as a dynamic interaction: a mutually beneficial connection between two autonomous fields that changes direction over time. I discuss the nature of this interaction and give a case study highlighting interactions between research on Bayesian networks in machine learning and research on causality and probability in the philosophy of science

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10.1023/b:mind.0000045990.57744.2b

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Jon Williamson
University of Kent

Citations of this work

Formal Epistemology Meets Mechanism Design.Jürgen Landes - 2023 - Journal for General Philosophy of Science / Zeitschrift für Allgemeine Wissenschaftstheorie 54 (2):215-231.
A Falsificationist Account of Artificial Neural Networks.Oliver Buchholz & Eric Raidl - forthcoming - The British Journal for the Philosophy of Science.

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References found in this work

Causality.Judea Pearl - 2000 - New York: Cambridge University Press.
Causality and explanation.Wesley C. Salmon - 1998 - New York: Oxford University Press.
Probability Theory. The Logic of Science.Edwin T. Jaynes - 2002 - Cambridge University Press: Cambridge. Edited by G. Larry Bretthorst.
Computing Machinery and Intelligence.Alan M. Turing - 2003 - In John Heil (ed.), Philosophy of Mind: A Guide and Anthology. New York: Oxford University Press.

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