What subjects, topics does a computer science graduate need to learn to apply available machine learning frameworks, esp. SVMs
From the theory end, you should understand how SVMs work (and why they do).  For example, I like the scribe notes from this class for a quick explanation and something like this tutorial for a more in-depth one.  You might also want to consider other machine learning techniques that are suited for binary classification on large data ie. boosting, random forests, ...

As for dealing with open source code issues and with parsing HTML, stackoverflow might be a better place to seek answers.

EDIT:  Given your modified question, I can address your concrete questions about SVMs directly:


$R^n$ "stands for" the fact that the (data) points to be classified lie in a $n$-dimensional Euclidean space.  For example, points in our familiar $2$ dimensions (with, say, latitude and longitude coordinates) lie in $R^2$ -- an $n$-dimensional space generalizes this notion.
A hyperplane (in $R^n$) is a flat of dimension $n-1$.  It is what an SVN uses to classify the data (after they have been mapped to the higher dimensional space by the chosen Kernel).  For example, if your space is $R^2$ (or a plane) then a hyperplane is line, and so on.
How to transform the space? This is done via the Kernel function.  A big part of SVM design is choosing a proper Kernel for the problem at hand.


To understand these three points in more detail, you can read the references above.