Machine Learning(ML) has an overarching impact, yet misconceptions about its fundamentals result in unrealistic expectations. This highlights the need for targeted educational research on how to effectively teach ML to both professionals and the general public. This thesis explores the knowledge required to learn ML, with a focus on mathematical skills, their impact on learning, and strategies for adapting instruction to different levels of math proficiency.

With the fast integration of Machine Learning (ML) across industries, effective pedagogical strategies are essential for teaching this complex and evolving field. Machine Learning is now widely integrated into various university programs and introduced at earlier educational stages, including high school and secondary school. However, ML pedagogy lacks standardized teaching methods compared to other science-related subjects, which have established norms for topic introduction, teaching tools, and assessment methods. Inspired by other fields, this research explores the use of interactive visualizations in teaching ML topics, more specifically in teaching Gradient Descent (GD) and Principal Component Analysis (PCA). The target population consists of Computer Science and Engineering Bachelor students who have not yet followed any Machine Learning courses but have foundational knowledge in calculus, linear algebra, and statistics. The evaluation measures knowledge gained and student motivation, compared to a static version of the materials. Results show a significant positive effect in knowledge related to PCA with interactive visualizations, but no differences in knowledge gain for GD or in learning motivation for either topic. With these results, we contribute to the body of evidence-based teaching methods in Machine Learning and identify further research needed to generalize the effect of interactive visualizations as a teaching method for teaching ML basic concepts.