We spent some short time in discussing resume projects. But then we spent the rest of time discussing a single ML model in depth. From data formulation, processing to how the model works to regularization methods. So I say you have to at least review the models to a complete graphic level and shallow-ish math equation level in order to answer the questions. (BTW the interviewer will expect a "correct" answer) How Linear regression works? How are the weights updated? How by adding a regularization term can reduce overfitting? (Think mathematically) And questions like how to deal with imbalanced labels? (I provided 2 solutions but the interviewer was expecting something else, and honestly we don't learn that in graduate school...)
