questions from memory:
1) data preprocessing - what is it, why?
2) bagging - boosting? what is it, which algorithms use it?
3) what are the concepts "linear separation" & "soft margins"? which classifiers use it?
4) what is the concept "landmarking"
5) practical: decision tree & bayes net (design & calculate bayes net, than classify a new instance, what happens if node "debt" is instantiated)
since as far as i can remember we never actually built a bayes net from scratch (with a dataset and probabilities), the 5th question was really hard for me...