Bridging the Knowledge Gap

Identifying Essential Machine Learning Concepts for Effective Progression in Follow-Up Courses

Bachelor thesis (2023)

Authors

L.J. Jongejans Electrical Engineering, Mathematics and Computer Science

Contributors

M.A. Migut Web Information Systems - EEMCS (mentor)
Faculty
Electrical Engineering, Mathematics and Computer Science, Electrical Engineering, Mathematics and Computer Science
Copyright: © 2023 Liselotte Jongejans
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Published Date

28-06-2023

Language

English

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Faculty

Electrical Engineering, Mathematics and Computer Science

Abstract

This research paper aims to investigate the adequacy of concepts taught during an introductory machine learning course in preparing students for subsequent courses and their professional careers. The study adopts a comprehensive approach, including a literature review, interviews with teaching staff of follow-up courses, and a survey administered to students. The findings of the research indicate a homogeneity in the results, with no significant knowledge gaps identified in the concepts covered by the ML course. However, the study highlights the importance of emphasizing the underlying mathematical foundations more prominently, to enhance understanding and application in real-world scenarios.

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