It depends on what you want to do as a researcher. If you want to prove theorems in machine learning theory, courses like convex optimization make sense. I would also add statistical machine learning (CMU 10-702) for a broader view.
But a large part of ML research is not theoretical stuff, and involves building real systems. And in that case you should get a cursory overview of ML and then focus on the subdomain you may be interested in. By cursory overview I mean getting a gist of things like graphical models, SVMs etc.- a typical entry level grad ML course. This should be enough when you delve deep into your domain of choice.
Not sure this is good advice - without background I have mentioned there is no chance of actually making sense of the material in the CMU course - note how linear algebra is a prerequisite and they cover convex optimization at the very beginning of the class (even though it is not possible to do the subject justice in 2 lectures).
Yes, you are correct. Sorry for the confusion; I meant that courses like 10-702 should be the star course he should work at, to have some short at understanding and contributing to theory heavy side of ML.
The first basic course for the latter approach can be 10-601, it's a machine learning course meant for senior undergrads. Look at schedule and assignments.
Also since you have interest in text mining etc. and if you want to focus on that, you can skip a lot of courses, and go directly to language and statistics-1. It's a great course if you wish to work primarily with text, and it covers the prerequisite ML.
If you are interested in a course, save it's slides and assignments. They will be down in a few months. They keep recycling the course sites whenever a new semester starts.
But a large part of ML research is not theoretical stuff, and involves building real systems. And in that case you should get a cursory overview of ML and then focus on the subdomain you may be interested in. By cursory overview I mean getting a gist of things like graphical models, SVMs etc.- a typical entry level grad ML course. This should be enough when you delve deep into your domain of choice.