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!

closed as off-topic by jimm101, gnat, scaaahu, mcknz, Masked Man Sep 27 '16 at 12:13

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You are massively employable. Either way you are going to start at a very good salary. You are going to have multiple job offers. And your skills are not going to fall out of demand.

Go with what you want is of most interest to you.

Grad course with implement on a team would better present you as a guy ready to hit the ground running.

If the grad course is a renowned prof and you get to work with some really bright student then maybe go that route.

If you might want to go into research then go into research then take the research option.

I know this going to sound whacked but with video and audio content you could work with government intelligence if that is of interest to you.

Go with your heart. At this point you don't need to worry about building a resume.


I think this piece of advice from Paparazzi is very good: "Go with your heart."

That being said, here's how I would approach this. It sounds like the research opportunity is a unique experience that would be difficult/ impossible to do on your own. If you could get a publication, that would look far better on a CV than a single graduate course. The grad course sounds more generic and is possibly something you can do as a side project if you are highly motivated.

Also, consider this. You are entering a very competitive field, data science, where most of us have at least a masters degree. While it certainly sounds like you are qualified for any machine-learning related job, many employers will filter out anyone without a graduate degree. With this in mind, I think the research opportunity would look better on your CV if and when you apply to grad school. Plus, research is a great way to network and secure a recommendation.

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