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I have admits from CMU LTI's MS in Intelligent information systems and MS CS at UT Austin. Which should I choose?


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My area of interest is Machine learning and its applications.

Carnegie Mellon University's MS in Intelligent Information systems is a professional program offered by the Language Technologies Institute, part of their School of Computer Science. It focusses on analyzing unstructured data in the form of text, vision or speech. It allows me to (only) take courses in the areas of ML, NLP, IR, Comp. Vision, along with capstone project and directed study with a prof there.

UT Austin MS CS is of course broad, but apart from a couple of diversity courses I can do similar courses as above on ML, NLP, IR. The course offerings in these areas are still lesser than at CMU. But it's pretty much free (because of great aid scene, so a difference of at least 60-70k ) and offers a traditional MS CS degree.  

My goal is to be a software engineer who works on problems in the areas of ML, NLP and IR. While I am not entirely sure about it, I would probably not want to go for a pure data scientist role. 

Factors that I am concerned about are:

1. If I am going to do more or less the same courses both places, I think CMU courses are going to be better. Is the advantage of UTA only the name of MS CS? How important is that? 

2. Will it matter in the long run to have done a broader study through MS CS? 

3. Will job opportunities at both universities be similar? Will CMU's course restrict my job opportunities? 

4. Should the industry reputation or 'brand' of CMU be a factor? How good is UTA's reputation?

5. I hear that the courses at CMU are very rigorous, and this is a great learning opportunity. There are also many leading faculty there. How does UT Austin fare with regard to rigor of the program

 

6. Is it worth to be spending on more than 60-70k for CMU when UTA is also a top 10 program. By difficulty of getting in, UTA is definitely more difficult than this program in CMU.

 

What would you suggest and reasons please.

Thanks!

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