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Demand for Machine Learning (ML) courses remains high, and educators face open questions about which prerequisites are important for student success in upper-year ML courses. Prior work has shown that instructors and students in ML courses believe that the math prerequisites and their relative recency are barriers to success, but this relationship has not been demonstrated quantitatively. In this paper, we study the link between prerequisite grades and performance in an upper-year ML course at two sites. We use linear models to study the extent to which student grades in prerequisite courses in calculus, linear algebra, statistics, and software design are predictive of student performance in the ML course. We consider the effect of additional factors like gender, first-in-family status, prior experience, comfort with mathematics, and comfort with academic English. Like prior work in many domains, and consistent with ML instructor and student perspectives, we find that prerequisite grades are predictive of ML performance. However, different combinations of prerequisites are important at different sites. Also, we find that cumulative grade point average (cGPA) in past technical and non-technical courses are as predictive of ML grade, if not more. Moreover, recency in prerequisite courses is not predictive of ML course grades in our setting. These findings suggest that general academic preparation may be as robust a predictor of ML course performance as specific math prerequisites, challenging assumptions about the role of mathematical recency and preparedness—at least as measured by grades.