Year: 2012
Type: article
Abstract: The field of machine learning is constantly developing useful new techniques for data analysis, but they are often ignored by researchers outside the field due to unfamiliarity and the difficulty of keeping up with a large body of work.We propose a methodology for training researchers how algorithms work through experience, such that they gain an implicit, rather than explicit, understanding of their function.Thus we combine theory from discovery learning with advanced software and a more educated target population to foster such understanding.We have developed an open source application for exploratory data analysis called Divvy that lets users quickly and visually interact with a range of data analysis techniques.Using a simplified version of Divvy, we find that undergraduate subjects are generally able to learn machine learning concepts through experience, though they have only partial success in applying them.
Authors: Lewis, Joshua, de Sa, Virginia
Institution: University of California San Diego
Language: English
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
SDG: Quality education
Open Access status: green
Funder: National Science Foundation
OpenAlex ID: W2396694237