Machine learning (ML) is increasingly integrated across diverse academic disciplines, necessitating effective teaching strategies tailored to varied student backgrounds. This study investigates the influence of prior mathematical knowledge on the learning outcomes of ML topics among Computer Science (CS) and Aerospace Engineering (AE) students. Employing a mixed-methods approach, the research involved initial mathematical assessments, interactive tutorials on key ML topics (Bayes Rule, Perceptrons, ML Pipelines), and subsequent evaluations of ML comprehension.

The results reveal significant differences in performance between the two groups. CS students, with their integrated programming and mathematical preparation, consistently outperformed AE students, who demonstrated variability despite their strong quantitative foundations. Probability and linear algebra emerged as key contributors to ML learning, showing stronger correlations with outcomes than calculus. Qualitative analysis highlighted the need for tailored instructional approaches: AE students preferred application-driven and interactive learning, while CS students valued structured and technically detailed resources.

These findings underscore the importance of interdisciplinary teaching strategies that bridge gaps in programming and mathematical competencies. The study’s insights have implications for designing inclusive ML curricula, emphasizing real-world applications, adaptive learning technologies, and frameworks to support diverse learner needs. Future research should explore broader ML topics, larger participant groups, and long-term skill retention to further enhance ML education across disciplines.