Philosophy through machine learning
Publication
, Journal Article
Lim, D
Published in: Teaching Philosophy
March 1, 2020
In a previous article (2019), I motivated and defended the idea of teaching philosophy through computer science. In this article, I will further develop this idea and discuss how machine learning can be used for pedagogical purposes because of its tight affinity with philosophical issues surrounding induction. To this end, I will discuss three areas of significant overlap: (i) good / bad data and David Hume's so-called Problem of Induction, (ii) validation and accommodation vs. prediction in scientific theory selection and (iii) feature engineering and Nelson Goodman's so-called New Riddle of Induction.
Duke Scholars
Published In
Teaching Philosophy
DOI
EISSN
2153-6619
ISSN
0145-5788
Publication Date
March 1, 2020
Volume
43
Issue
1
Start / End Page
29 / 46
Related Subject Headings
- 2203 Philosophy
- 1302 Curriculum and Pedagogy
- 1301 Education Systems
Citation
APA
Chicago
ICMJE
MLA
NLM
Lim, D. (2020). Philosophy through machine learning. Teaching Philosophy, 43(1), 29–46. https://doi.org/10.5840/teachphil202018116
Lim, D. “Philosophy through machine learning.” Teaching Philosophy 43, no. 1 (March 1, 2020): 29–46. https://doi.org/10.5840/teachphil202018116.
Lim D. Philosophy through machine learning. Teaching Philosophy. 2020 Mar 1;43(1):29–46.
Lim, D. “Philosophy through machine learning.” Teaching Philosophy, vol. 43, no. 1, Mar. 2020, pp. 29–46. Scopus, doi:10.5840/teachphil202018116.
Lim D. Philosophy through machine learning. Teaching Philosophy. 2020 Mar 1;43(1):29–46.
Published In
Teaching Philosophy
DOI
EISSN
2153-6619
ISSN
0145-5788
Publication Date
March 1, 2020
Volume
43
Issue
1
Start / End Page
29 / 46
Related Subject Headings
- 2203 Philosophy
- 1302 Curriculum and Pedagogy
- 1301 Education Systems