Fundamental Research in Machine and Deep Learning

This course is about doing fundamental empirical research in machine/deep learning. It is about doing research to better understand ML/DL models, what assumptions they make, when they fail, how to pose research questions, answer empirical hypotheses experimentally and analyze results.

Machine/deep learning research goes hand-in-hand with designing applications, superior performance, more data, improved accuracy, faster inference, bigger models, better algorithms, solving a problem, creating a new artifact/tool/AI, etc. While these concepts are all valuable in their own right, they are NOT the topic of this course. This course is NOT about applying ML/DL to "solve" some problem. Instead, here, we study how to "understand" and do fundamental empirical research in ML/DL itself. This course is about repeatedly asking "Why?"; it's about better understanding, reflecting, questioning, reproducing, and analyzing ML/DL research.

Students will

• read, present and debate scientific papers on machine learning scholarship,

• construct a logical research "storyline", see: https://jvgemert.github.io/storyline.pdf

• create synthetic controlled experiments, see: https://controlledexperimentsinml.org/

• reproduce existing ML/DL research, see https://reproducedpapers.org/

  while honing a critical attitude and being able to communicate concisely and clearly.