What math courses should I revisit to prepare for a CS Ph.D. (Reinforcement learning and Game Theory)? I am planning to join a CS Ph.D. program in 6 months. My topics of research will be in the area of Reinforcement Learning and Game Theory. Even though I have a good grasp of these two topics’ applications and empirical side, I struggle with the theory a lot when reading papers. For example, It is taking me a lot of time to understand the regret analysis of advanced Bandits. I have taken most of the essential math courses during my undergrad, including Differential + Integral + Multivariate calculus, Linnear Algebra, Probability, Stat, etc. But I feel most of those courses did not go deep enough. So I want to revisit some of my undergrad math and would also like to learn additional topics. Given my area of Ph.D., I would like to know the essential math topics I should study in the next six months to have a good grasp on the theoretical side and do well in graduate-level courses consistent with such a Ph.D.
Know linear algebra well, say, at the level of Peter Lax' book (start with the first 9 chapters). Also, some basic real analysis and probability theory should be a good place to start.