There's lots of preparatory ML reading lists, e.g. metacademy.org roadmap, Goodfellow et al. DL book, Shalev-Shwartz/Ben-David ML text (both books freely availa

There's lots of preparatory ML reading lists, e.g. metacademy.org roadmap, Goodfellow et al. DL book, Shalev-Shwartz/Ben-David ML text (both books freely available content), review materials on Stanford course syllabuses by Socher, Ng and Karpathy. Usual recs are Strang or Axler Linear Algebra, Ross or Tsitsiklis for Probability, Spivak Calculus, Boyd Convex Optimization, and other folks will recommend real analysis, diff eq's, topology, etc.