I recently made the decision to pursue graduate studies and apply for an MS in computer science program during next year's application process. More specifically, the field that I want to study is machine learning. However, there is something that's been concerning me a lot for the past few days.

I'm actually taking a machine learning course this semester, and I don't believe I did too well. I think I'll end up with either a C+ or at best a B or - if the professor feels very generous - a B+. If I were to give an excuse for the result, I'm not too familiar with many of the tools we used in class (Python, Sci-kit Learn, etc.) and the professor, TA's, and fellow students all gave me the same answer of "it's your job to learn on your own" when I would ask questions (which is true to an extent). It was a disheartening semester, to say the least. But excuses aren't important, and the result is that I didn't perform well.

Now the actual question is: Would this affect my graduate studies application? My GRE scores are satisfactory (163V/169Q/5.5AW) and my TOEFL scores are also good (118/120). Upon graduation I will have around a 3.2-3.4/4.0 GPA.

I've heard stories of professors rejecting applicants because they performed poorly for relevant courses despite showing positive traits in other ways, and I guess that's been concerning me.


Much of this will come down to what types of schools you want to apply to and what type of funding you hope to receive for graduate school.

In the case of the graduate students we review at my school, GRE is an initial filter for classifying applicants as "acceptable" or "not acceptable." After that we do not look at GRE scores much. This means that a high GRE score gets you in the door and essentially nothing more. Letters of recommendation and grades in relevant classes are much more important to us. Your GRE scores are quite high. Unfortunately, graduate school success usually has little to do with how well you can perform on a single 4 hour test. Grades are a better reflection in my experience. (Especially subject-specific grades).

I will be blunt: A C+ in a highly relevant course is not a good sign. There is no magic formula to overcome this. Admission committees will not really look highly upon a candidate who gets a C+ in a machine learning class because they could not learn Python. If you couldn't do it as an undergrad, what indicators are there that you will suddenly be blessed with the gift to learn Python and SK-Learn as a graduate student?

Understand that I'm not trying to be overly harsh on this. I am conveying the questions that an admissions committee must ask.

I will add that sometimes we will admit an MS student who pays his/her own way. This is usually done in the case where we see a person who has potential, but not a lot to show for it. If their grade issues persist as a graduate student, it's not really a huge issue for us. They usually self-select to drop out of the program.

  • Hello. Thank you for the answer. Everything you said I agree with wholeheartedly. Fortunately, I was able to pick up Python and the relevant tools/libraries I needed, but it was a bit too late as the first assignment is what was the most heavily weighted. On a side note, I am planning to work on projects/research with other peers and/or institutions. I know it's a bit of a subjective question, but if I am able to demonstrate competence via those channels, would that be able to compensate for a poor grade? – Seankala Dec 17 '18 at 17:41
  • @Seankala Demonstrated competence via research or outside projects is always a plus. It would definitely factor favorably into your application. – Vladhagen Dec 17 '18 at 17:45

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