Also, the notes assume a reasonable knowledge of probability theory and linear algebra. If you're unsure, you might do well to review those topics before approa

Also, the notes assume a reasonable knowledge of probability theory and linear algebra. If you're unsure, you might do well to review those topics before approaching machine learning (or at least keep good references handy). (Edit: the link above has Section Notes that review probability theory, linear algebra, and convex optimization. You might find those useful.) And it's not an easy course. Don't be discouraged if the problem sets seem impossible. (They very nearly are.)