How Machine Learning Impacts the Undergraduate Computing Curriculum

Tools

Shapiro, R. Benjamin; Fiebrink, Rebecca and Norvig, Peter. 2018. How Machine Learning Impacts the Undergraduate Computing Curriculum. Communications of the ACM, 61(11), pp. 27-29. ISSN 0001-0782 [Article]

No full text available

Text
CACM viewpoints_ ML Ed is CS Ed.pdf - Accepted Version
Permissions: Administrator Access Only
Available under License Creative Commons Attribution Non-commercial.
Download (85kB)

Official URL: https://dl.acm.org/authorize?N663463

Abstract or Description

Machine learning now powers a huge range of applications, from speech recognition systems to search engines, self-driving cars, and prison sentencing systems. Many applications that were once designed and programmed by humans now combine human-written components with behaviors learned from data. This shift presents new challenges to computer science (CS) practitioners and educators. In this article, we consider how machine learning might change what we consider to be core computer science knowledge and skills, and how this should impact the design of both machine learning courses and the broader CS university curriculum.

Item Type:

Article

Identification Number (DOI):

https://doi.org/10.1145/3277567

Additional Information:

© ACM, 2018. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in Communications of the ACM, {VOL 61, ISS 11, (November 2018)} http://doi.acm.org/10.1145/3277567}

Keywords:

Machine learning, Computer science, Undergraduate computing curriculum

Related URLs: