[eli_awry]

I've spent the last 1.5 years as a machine learning PhD student slowly discovering many of these resources and topics, and I wish I had had this list at the beginning - it contains most of the gems I've found. I'd add that PGM course on Coursera clearly explains fundamental topics in probabilistic graphical models.

It's important to understand individual algorithms, but in many ways it's more important to have a broad overview of the field and its more modern methods, so that given a problem it's possible to think about the best way to solve it, and to share a common language with others who may have ideas. Beyond this list and various online courses, I've found that talking to people about their work and explain the high-level concepts of every black-box classifier or similarity metric or whatever it is they use has been quite educational