Machine learning (ML) has become a critical skill across various disciplines, yet teaching it to students outside Computer Science and Engineering (CS) remains challenging due to differing academic backgrounds. This study investigates the differences in learning outcomes between Industrial Design (ID) and CS students when introduced to foundational ML topics, focusing on the influence of prior mathematical knowledge.

Through initial surveys on mathematical proficiency, structured ML tutorials, and final assessments on learning outcomes, the research examines correlations between mathematical proficiency and ML performance, faculty-specific challenges, and qualitative feedback on learning experiences. Results reveal that prior mathematics knowledge significantly impacts performance on mathematics-intensive topics such as Bayes' Rule, while its influence is minimal on less math-relevant topics like ML pipelines. Furthermore, ID students emphasized creative and interactive teaching methods, contrasting with the programming-oriented preferences of CS students.

These findings highlight the need for interdisciplinary instructional strategies that cater to diverse learner strengths. By uncovering faculty-specific patterns in ML learning, this study contributes to the design of more inclusive and effective educational practices, fostering a broader understanding and application of ML across disciplines.