Sure. Have you looked at the PGM class on coursera for CRFs? The slides are a great condensed version of koller's book on PGMs. NLP is a really easy context they are used in. With that, I would recommend the stanford NLP coursera class (the first one from a few years back) that covers viterbi, CRFS, and general sequential models.I know I'm recommending a lot of MOOC content here, but I would add here that I think they provide a great overview from which to break in to the papers/other literature on the topic.
For SVMs, outside of the typical research you'd do on wikipedia, I unfortunately haven't had much specific experience with SVMs. I used them quite a bit a few years back for relation extraction and other algorithms, but I'm mainly from an NLP background in that context.
Thanks! This is helpful. I'll check both out courses.
The recommendation for PGM sounds promising. I am familiar with Bayes theorems (through the first offering of the AI course on what later became Udacity).
I had signed up for NLP on Coursera but dropped in it the middle since I was already familiar with the contents till that point (having read most parts of both Christopher Manning's and Dan Jurafsky's books already). I'll check the materials for the rest of it.
For SVMs, outside of the typical research you'd do on wikipedia, I unfortunately haven't had much specific experience with SVMs. I used them quite a bit a few years back for relation extraction and other algorithms, but I'm mainly from an NLP background in that context.