Advantages of Prior Mathematical Knowledge for Studying Machine Learning
Differences in Knowledge Gain between Computer Science and Physics Students
O. Hageman (TU Delft - Electrical Engineering, Mathematics and Computer Science)
I.E.I. Rențea – Mentor (TU Delft - Web Information Systems)
M.A. Migut – Mentor (TU Delft - Web Information Systems)
J.H. Krijthe – Graduation committee member (TU Delft - Pattern Recognition and Bioinformatics)
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Abstract
With the growing need for machine learning knowledge for many different expertises and positions, comes a growing need for machine learning education for non-computer scientists. Teaching machine learning concepts to non-majors comes with the added challenge of dealing with different levels of prior mathematical knowledge. Existing research is inconclusive on the correlation between this prior knowledge and topic-specific machine learning knowledge gain. This paper evaluated this via an experiment conducted on Computer Science and Physics students without prior machine learning education. We find that there is no clear correlation between general math knowledge and knowledge gain. There is however a clear correlation of proficiency in probability and statistics, and algorithm heavy machine learning topics. The experiment also concluded that most students struggled most with these math-heavy topics, as well as understanding abstract systems such as perceptrons.