Semantic Scholar Open Access 2019 537 sitasi

Causality for Machine Learning

B. Scholkopf

Lihat Sumber DOI

Abstrak

Graphical causal inference as pioneered by Judea Pearl arose from research on artificial intelligence (AI), and for a long time had little connection to the field of machine learning. This article discusses where links have been and should be established, introducing key concepts along the way. It argues that the hard open problems of machine learning and AI are intrinsically related to causality, and explains how the field is beginning to understand them.

Topik & Kata Kunci

Computer Science Mathematics

Penulis (1)

B. Scholkopf

Format Sitasi

APA MLA BibTeX

Scholkopf, B. (2019). Causality for Machine Learning. https://doi.org/10.1145/3501714.3501755

Akses Cepat

Lihat di Sumber doi.org/10.1145/3501714.3501755

Informasi Jurnal

Tahun Terbit: 2019
Bahasa: en
Total Sitasi: 537×
Sumber Database: Semantic Scholar
DOI: 10.1145/3501714.3501755
Akses: Open Access ✓