Visualizing Complexity: Kernel Density Estimation in University Education

Investigating Misconceptions, Challenges, and the Role of Prior Knowledge in Comprehending KDE

Bachelor thesis (2023)

Authors

T.G. Popica Electrical Engineering, Mathematics and Computer Science

Contributors

M.A. Migut Web Information Systems - EEMCS (mentor)
C. Lofi (graduation committee member)
Faculty
Electrical Engineering, Mathematics and Computer Science, Electrical Engineering, Mathematics and Computer Science
Copyright: © 2023 Tudor Popica
More Info
expand_more

Published Date

27-06-2023

Language

English

Reuse Rights

Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.

Faculty

Electrical Engineering, Mathematics and Computer Science

Abstract

This research investigates the improvement of Kernel Density Estimation (KDE) comprehension in a university context via visualization-enhanced teaching. The study tackles KDE misconceptions, the efficacy of visual aids, and the role of previous mathematical and machine learning knowledge. Using a mix of literature review, survey, and experimental study, findings show that visualization techniques notably enhance KDE understanding and application, validating our hypothesis. This work underlines the importance of evidence-based teaching in machine learning education.