This past semester I've learned quite a few things. From experience I've learned that TAN trees (Tree Augmented Naive Bayes) is not really better than the Naive Bayes Classifier. I've also learned that if you have lots of training data, then simple things like the nearest neighbour classifier work really well.
I learned a lot of new material from Carlos Guestrin's Graphical Models class. Unlike other classes in the past, this one really did present me with a plethora of new material. Junction Trees (aka Clique Trees) are really cool for exact inference. It was also really nice to see approximate inference algorithms such as loopy belief propagation and generalized belief propagation in action. Overall I think I'll be able to apply some concepts from Conditional Random Fields and approximate inference into my own research in object recognition.
I also finished my Teaching Assistant requirement. I'm still amazed at the high quality of Martial Hebert's Computer Vision course -- the students that take that class are almost ready to start producing research papers in the field. If there is one thing that sticks out from that course is how a large number of seemingly distinct problems get linearized and formulated as eigenvalue problems.
On another, not I started reading Snow Crash by Neal Stephenson. Pretty standard cyberpunk/hacker literature -- reminds me of World of Warcraft even though I've never played the game. I like it very much so far -- I should be done in a couple of days.