ev a.

Being published would help A LOT. Being in the process of to be published can help too. Do you have anything to show for of your work? Maybe a contribution to the research of your professor that s/he can vouch for, or show you as the primary researcher in that work? UIUC is good, but not on the same league as the others you mentioned. You have reasonable chance (since you'll be paying the tuition/or your employer) on this one, but even with great grades and good research experience, you would be lucky to be admitted to those others. The area is very competitive and even the best students are slayed in the process. Definitely apply to all you want to attend, what you gonna lose, application fee? But don't have high hopes, you want NLP and ML, best brains are pouring to that area, hence in any case, you'll have low probability of being admitted, regardless of your profile. If you have an option to add meaningful contribution to your research in this limited time period, focus on that instead of GRE's. GRE's are stupid, Q questions are easy, and V is not even considered in most cases (for good reason). Aside from it being a quick filter for weeding out the grade inflation that many schools have (by putting cut-offs like Q>160 etc), no committee ponders over it. Best of luck to you!

I have the same GPA, no papers (one paper about computer vision in review), two A+ grades in graduate level Neural networks and Machine learning courses, and I haven't done basic computer engineering courses such as automata, OS or organization. I got rejected from Stanford MSc, UT Austin, and some others. I now realize that it was foolish for me to apply to those schools because of the lack of fundamental courses as well as the research experience. DO NOT only apply for the top. Your list is incredibly ambitious, if you can spend the money and have understanding referees, apply for these schools plus 3-4 less ambitious programs. Also, note that the area you apply is the most popular area of research currently. Neuroscience has some parallels, but more mathematically involved (frankly harder) and does not exactly translate to the ML.

What is the area? Last year, adcomms slayed the ML applicants (including myself), but theory, for example, is much easier to get in in comparison.

If you read papers as a hobby, and you have the skills to implement the concepts in there, you may have a good shot. I only started my graduate degree last semester, but it is extremely rare for a student to read papers for fun in my experience. Top companies employ people who can read actually papers and make use of them. As for your profile, it is weak but I think relevant experience and work can compensate for that. If ranking is not important, it shouldn't be too much of a problem. Good Advisor is not subjective, it's mostly the chemistry that differentiates between good and horrible. On that front, it is your job to thoroughly search for the right person (choosing the professor instead of the higher ranked school is always wiser).

@rising_star and @svent, thank you both for your responses. As a last question, do you believe that it would be tough for a person with only an MSc degree to get jobs at good companies? Obviously good is very relative, I generally mean companies like Apple, Google, Facebook, or start-ups on the rise, basically enough for me to make a decent living as an engineer.

Thanks, care to elaborate?

Masters, not PhD. I am an international student. I've been admitted to some places, including EPFL with scholarships, ETH Zurich, UIUC. I hope to work at Silicon valley, hence USA is very appealing, but Illinois @ Urbana-Champaign did not grant me any scholarship. I am afraid that securing a job from Europe would be harder, as some of my friends are struggling right now with that very same issue. Illinois said that it was possible (but not a guarantee) for me to work as a TA to cover some of the expenses. I would be declining excellence scholarship from another school if I go to UIUC. I am not much concerned about the educational quality, all seem fine to me. Is going to UIUC a good investment for future job prospects? (Still waiting to hear from UT Austin btw)

@robot_control Sure. EE is asymmetrical, in the sense that a lot of work goes in, and nothing comes out from the point of an undergraduate student. Perspiration does not translate to an award, let that award be work satisfaction or an actual product. By far the largest project that I did was a 56k-modem that was implemented on an FPGA, whereas you can do a lot more and meaningful sh*t in another area, such as CS. Of course, this is obviosly grossly subjective, and for some others works is perfectly satisfying and enjoyable. As a recent graduate, I only can think how much more I could do as a CS student and an undergraduate researcher in that same 4 year span. (note: these are the ramblings of an angry ms candidate who just received some bad news, please don't take it seriously if you wish to pursue a career in engineering -- it's lovely)

@robot_controlThank you for your response. Actually in my school's CS curriculum, I only lack 4 courses in undergraduate curriculum, and trying to catch up with courses mentioned above that are directly related with the area. The reason I did not lean towards the CS area before is that EE curriculum is incredibly full as it is, and in fact the 5 additional courses on top of those was almost an unheard of, as far as I know. BTW, if your interviewer's remark applies to another schools, then it's safe to say that I'm screwed, oh well, at least the rejection letter won't be a shock. I think I wouldn't applied to any other program even if I knew this horryfing statistic. I love the probabilistic mathematical nature of the topic, and the vision course (which includes 20-30% of pattern recognition material inside it) I already took was great, I learned so much, which I can't say for my EE courses.

@robot_control: mainly because the deep learning, neural networks and general ML and AI are in CS department in those schools that I applied to. Also, there is another psychological factor that I want to sever my ties with EE department as well

I am a student who recently graduated from university with 3.95/4 CGPA, have an electrical engineering degree, taken 5 additional courses to my curriculum to be able to fill some gaps in my CS knowledge such as programming languages, data structures, algorithms and computer vision courses. I am assuming my LOR's are very strong, as I have done projects and helped further the research of the professors I have worked with and they seemed happy to write a LOR on my behalf. I have applied to Machine learning and computer vision areas, and currently taking ML, neural networks and computational geometry courses in graduate level (i started as a "special admit" student to my current university to not waste time waiting a full semester --and god forbid, if rejected, it's a safety net: i still can finish my MS here in my hometown). I applied to Stanford, UT Austin, Illinois, Gatech and U of Toronto in Canada, and EPFL in Switzerland for a MS degree, mostly without financial aid as an option (no scholarships if admitted). Two things bother me: As almost all the top CS students rush into ML and AI, I feel it is harder to get an admit from these top-50 schools, especially with insufficient backgrounds (no OS course or formal CS research as of application date --currently I am working on a project under my MS advisor here), and with a non-CS degree. Did I aim too high and let my ego get the best of me? Thank you for reading.

This is a very controversial topic to begin with. It is not hard to foresee that for every reply this thread gets, some alumni or a current student will jump in and defy to the idea of how dare anyone assume their school is not working their graduate students to death, yet I only ask for subjective opinions. Are there any good and reasonably relaxed CS programs in Top 100? By relaxed, I don't mean zero sleepless nights and hardworking, but ability to maintain a balanced and healthy social life, along the graduate studies.

Hello, I feel pretty horrible about my scores. Not verbal, since I am not a native speaker hence did not expect a stellar record on that yet I believe my presumably two mistakes on quantitative really hurts my chances. Probably I can score 170 next time yet my verbal and AWA may drop. As a CS MS candidate (not PhD hence maybe these are not that horrible scores) with good rest of the profile (gpa lors sop etc) should I retake the exam?I feel my analytical writing and quantitative can really hurt me. Am I right? Is it worth to take another shot or should this suffice? I am applying to McGill, Toronto, UT Texas, U of Illinois at Urbana Champaign, EPFL, Delft. Not ivy league or top tier but pretty good schools I think. So I would like to max. my chances, but if GRE is only a filter for applications and these are above that threshold, I would probably be better off spending my time on other endeavours.

As a candidate who faced the similar dilemmas in the past few weeks, here are my thoughts. You CAN'T know which universities are in your reach without applying. The criteria for universities around the country or world is so multifarious that it is not possible to determine ranges, or universities or even programs for that matter. There are micro decisions you can make, such as getting admitted to a PhD is harder than MSc or M.Eng is easier and so on. But different adcomms have different priorities. In fact, they switch priorities as years go by, so you can't conclude "X university admits students with such and such backgrounds" etc. Some prioritize publications, some purely GPA and test scores, some Masters programs are terminal programs which are more lenient towards those who don't have a strong CS theoretical background, Stanford is an example for this. Nobody can know what type will be admitted. In fact, I am sure that if adcomms did their evaluations on another day, they could've come up with a whole different list. What increases your chances are, good scores and grades, publications, early applications, great statement of purpose letters and you then hope for the best. Also, a good source for what to expect from an application can be found from this site: http://bit.ly/1OUIV2z This survey has thousands of entries, and as you can see, the profile for admitted and rejected are incredibly diverse. With great undergraduate career and perfect test scores, research assistantships and internships, there are people who get rejected from a school, yet someone with an average or sometimes below average background is admitted with fellowships! Lastly, my sources for this comment are Quora, The Grad Cafe, hours of Google searching, my current professors and professors at the universities I hope to get admitted that I had email conversations.

Hello, I am going to graduate this January (2016), and since I will have about 9 months before going to my Masters (if, fingers crossed, admitted) I decided to take some courses on my current university, where I hope to improve my background. To furthet clarify, I have a free spring term that I can do nothing since most decent graduate schools accept new students only on Fall, so I plan to spend my time at Spring on the university taking some courses. I am an EE major but since I plan to have my Masters on CS, I think this additional course thing will be helpful. I plan to take area specific and general fundamental courses on CS. I have a good background, yet it is not possible for a EE major to satisfy CS fundamentals perfectly in his undergrad. So, I also cannot fulfill every requirement to be admitted into a program yet they say that it's OK, as long as the student is willing to catch up with his fundamentals while studying MS degree. Question. Should I mention on my SOP that I plan to do this? My only reservation is that I for some reason think they (admission committees) will not be happy about this, like having a backup plan.