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oai:keep.lib.asu.edu:node-1988462025-02-18T18:30:04Zoai_pmh:alloai_pmh:repo_items

198846 https://hdl.handle.net/2286/R.2.N.198846 http://rightsstatements.org/vocab/InC/1.0/ http://creativecommons.org/licenses/by-nc-sa/4.0 2024-12 35 pages Brannen, Evelyn Ghayekhloo, Samira Chavez Echeagaray, Maria Elena Barrett, The Honors College Computer Science and Engineering Program Text This honors thesis introduces an interactive 3D visualization tool designed to enhance the educational experience of learning machine learning (ML) algorithms. Traditional methods for teaching ML, such as textbooks, static diagrams, and pre-recorded visualizations, often fall short in engaging students and conveying complex, iterative processes. This project bridges these gaps by enabling students to interact with foundational supervised and unsupervised learning algorithms, including Binary Classification, K-Nearest Neighbors, K-Means, and K-Means++ clustering. Built using Unity and C#, the tool provides an intuitive interface that allows users to manipulate parameters, visualize real-time outcomes, and explore high-dimensional data in a 3D environment. By enabling active, hands-on learning, this project aims to improve comprehension of ML concepts and promote engagement, particularly for students new to the field. Machine learning Data Visualization Educational Tools Unity Interactive 3D Data Visualization for Machine Learning Algorithms