Work

Broader and Earlier Access to Machine Learning

Year: 2016

Type: conference-paper

Abstract: This poster presents the results of an ongoing project that has developed learning modules to provide access to the tools and techniques associated with machine learning to a broad category of undergraduate students. The changing nature of science and its reliance on massive data sets has led to the integral use of machine learning approaches in just about every discipline. Recognizing this shift, flexible teaching materials have been developed to provide educators and students in a wide variety of academic fields with relevant machine learning topics and discipline-ready activities. Goals, module design, example material, dissemination plans and evidence of student learning are presented.

Source: ACM Conference on Innovation and Technology in Computer Science Education (ITiCSE)

DOI: 10.1145/2899415.2925485

Authors: Thomas Way, Lillian Cassel, Paula Matuszek, Mary-Angela Papalaskari, Divya Bonagiri, Aravinda Gaddam

Institution: Villanova University

Language: English

Topic: Statistics Education and Methodologies

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

SDG: Quality education

Open Access status: bronze

Funder: National Science Foundation