Disclaimer: I'm new here, I'm not sure if this fits this StackExchange, so please bear with me! I apologize if this isn't the right place.
I'm an undergrad CS major at a top tech school. I've already completed an internship at a big tech company and will be looking for full-time jobs next summer/fall. This semester, I have to choose between doing machine-learning research and a deep-learning grad course (I can't squeeze in both). Which one should I do?
Research: It focuses on analyzing the video and audio content of videos to understand what's in them, employing a variety of machine-learning approaches from deep learning to supervised feature-based methods. I might get a publication from this, should I choose to do this for a year. The coding work will be in Python (Theano maybe). Stuff that goes on my resume: the research experience itself + potential publication.
Grad course: It's about deep neural nets with assignments where we develop stuff like ConvNets and RNNs from scratch. There's also one final team project where we take a state-of-the-art paper in the literature of deep learning (stuff like ResNets, WaveNets etc.) and implement it in code by ourselves, reproducing or bettering the results of the paper. It's pretty similar in content to Stanford's CS 231N, except it covers stuff beyond ConvNets and focuses on other applications like speech recognition. Stuff that goes on my resume: the grad course itself + iPython course assignments on GitHub + TensorFlow final project on GitHub.
Which one of these two would be more useful to have on my resume for a full-time machine learning engineer job? Thanks!