I'm trying to learn more about kernel machine theory and I've discovered that I need to learn a lot of background math, and so I'm looking for some good resources for this. In particular: I've got Schölkopf and Smola's Learning with Kernels book and they start discussing Fourier transformations, Green's functions, operators (e.g. I've never heard of a pseudo-differential operator before), and other such things. I have no experience working with any of this but I really want to understand it. While I can certainly google individual examples I would really prefer to have a more comprehensive treatment.
Sorry if this is too vague or specific, but I'm really struggling with finding out how to start systematically acquiring the background math so that I can comfortably work with kernels and RKHS theory. Thanks a lot.
Update: I kept my background out because I was afraid that it would make this too specific to me, but because it was asked: I've taken one course in real analysis and one course in modern algebra, as well as a standard linear algebra and multivariate calculus course. I have not studied differential equations. I've also taken a number of courses in mathematical statistics (including some measure-theoretic ones, although I've never formally studied measure theory). I'm comfortable with the narrow range of statistics that I've studied (e.g. LLN, CLT, exponential families, GLMs, mixed models, complete and sufficient statistics, ...), but I don't have much of a pure math background which I feel is starting to hurt me.