A symbolic determinant under rank-one restriction computes a polynomial of the form det(A0 + A1y1 + … + Anyn), where A0, A1, …, An are square matrices over a field F and rank(Ai) = 1 for each i ∈ [n]. This class of polynomials has been studied extensively, since the work of Edmonds (1967), in the context of linear matroids, matching, matrix completion and polynomial identity testing. We study the following learning problem for this class: Given black-box access to an n-variate polynomial f = det(A0 + A1y1 + … + Anyn), where A0, A1, …, An are unknown square matrices over F and rank(Ai) = 1 for each i ∈ [n], find a square matrix B0 and rank-one square matrices B1, …, Bn over F such that f = det(B0 + B1y1 + … + Bnyn). In this work, we give a randomized poly(n) time algorithm to solve this problem; the algorithm can be derandomized in quasi-polynomial time. To our knowledge, this is the first efficient learning algorithm for this class. As the above-mentioned class is known to be equivalent to the class of read-once determinants (RODs), we will refer to the problem as learning RODs. An ROD computes the determinant of a matrix whose entries are field constants or variables and every variable appears at most once in the matrix. Thus, the class of RODs is a rare example of a well-studied class of polynomials that admits efficient proper learning.