When teaching in the classroom, a student will learn advanced chemistry more easily when previously taught fundamental principles. High school students learn about atoms, molecules, and their properties (e.g., electronegativity), while undergraduate students learn about nucleophilic substitution (e.g., polarized bonds due to electronegativity), stereochemistry, and frontier molecular orbitals (FMO). Then, graduate students will, for example, learn the Felkin-Anh model predicting the stereochemical outcome of some reactions, a model rationalized by the FMO theory. These different levels can be put into practice in ML method development by teaching these principles to machines to optimize their learning of advanced principles.