Big Data

Big Data and methods for analyzing large data sets such as machine learning have deeply transformed scientific practice in many fields. This Element defends an inductivist view of big data research and argues that the type of induction employed by the most successful big data algorithms is variational induction in the tradition of Mill's methods.

List of contents

1. Introduction; 2. Defining Big Data; 3. Inductivism; 4. Machine Learning as Variational Induction; 5. Correlation and Causation; 6. The Role of Theory; 7. Novel Scientific Methodology?; 8. Conclusion.

Summary

Big Data and methods for analyzing large data sets such as machine learning have deeply transformed scientific practice in many fields. This Element defends an inductivist view of big data research and argues that the type of induction employed by the most successful big data algorithms is variational induction in the tradition of Mill's methods.

Product details

• Authors: Wolfgang Pietsch, Pietsch Wolfgang
• Publisher: Cambridge University Press ELT
• Languages: English
• Product format: Paperback / Softback
• Released: 31.01.2021
• EAN: 9781108706698
• ISBN: 978-1-108-70669-8
• No. of pages: 75
• Series: Elements in the Philosophy of; Elements in the Philosophy of Science
• Subjects: Natural sciences, medicine, IT, technology > IT, data processing > IT; machine learning, SCIENCE / History, SCIENCE / Philosophy & Social Aspects, Philosophy of Science, History of Science, COMPUTERS / Data Science / Machine Learning, Data analysis: general, Data science and analysis: general