[D] PhD in theoretical ML or Deep Learning?
Hi. I am a soon-to-be ML PhD student. I have the option to choose between joining a theoretical ML group or a Deep Learning group (with application to CV/NLP). The work being done by the theoretical ML group is quite rigorous and basically about proving theorems all day with not much coding (example topics can be inference in graphical model, sample complexity, PAC learning, …). On the other hand, Deep Learning is so hot nowadays with so many cool applications (that also means more job/internship opportunities). Could anyone give me some advice on which group I should join? I am leaning toward the theoretical ML group because I feel like I can still make a switch to Deep Learning later on if I want to.