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.

Machine learning (ML) has become a vital skill across various disciplines, driving innovation and transforming industries. This growing demand emphasizes the need for effective teaching methods tailored to students with diverse academic and technical backgrounds. Teaching ML to non-majors presents significant challenges, as many students lack foundational knowledge in mathematics and programming. This study explores how university instructors addressing these challenges in bachelor’s and master’s level courses, based on semi-structured interviews. The analysis uncovered several key strategies used by instructors. Many emphasized connecting ML concepts to real-world applications, making the subject more relatable. Visualization tools were commonly employed to simplify abstract concepts and improve comprehension. Hands-on activities, including live coding and interactive assignments, were highlighted as effective methods to engage students and bridge theory with practice. However, instructors faced challenges as well such as accommodating diverse student backgrounds, correcting misconceptions, and designing assignments that balanced accessibility and depth. Although tracking student progress was not the focus of this study, some insights were provided. Some instructors mentioned using formative assessments, such as quizzes and project-based evaluations, to measure understanding. These findings highlight the importance of adaptable teaching methods and inclusive learning experiences in making ML education more accessible and effective for non-majors, while also providing actionable advice to improve the educational process and course design.

As Machine Learning (ML) continues to shape advancements in academia and industry, ensuring effective ML education is essential. This study examines the retention of four core ML concepts- Principal Component Analysis, Gradient Descent, Bayes’ Theorem, and Hierarchical Clustering- two years after students completed a university-level ML course. Using a survey-based methodology, it explores how prior mathematical knowledge, perceived difficulty, and confidence influence long-term retention. Results reveal a significant positive correlation between Calculus knowledge and Gradient Descent retention, with weaker correlations for Linear Algebra with PCA and Probability with Bayes’ Theorem. Perceived difficulty and confidence also shape retention outcomes. The findings emphasize the need for targeted mathematical refreshers in ML courses to strengthen foundational knowledge and improve retention. This research provides actionable insights for curriculum design, aiming to bridge mathematical gaps, enhance learning outcomes, and sustain student engagement with advanced ML concepts.