Learning math for ML from the top down or bottom up? • /r/MachineLearning

Hi all - I'm seeking advice on how to best learn the math required for doing machine learning research, particularly with regard to neural nets (and other graphical models - sorry if I'm using these terms incorrectly). My background is in cognitive science, but of a particularly computational flavor, so I've been exposed to the high level ideas behind "connectionist" models, and have used them as a sort of black box in the context of comparing their performance to human behavioral data. But my undergrad coursework is conspicuously lacking in math. I recently got a job as a software engineer in a lab that works on deep learning (in NLP applications), and I want to be able to understand the math well enough to contribute to research. The lab PI and I have discussed my interest in eventually converting to a grad student, so I want to make sure my math abilities are solid as soon as I can.