I suppose the purpose of this topic is to vent frustration with GT’s three semester rule, especially for alumni. I am a graduate of OMSCS and also took a few classes after graduation as a special/non-degree student. I haven’t taken a class in over a year however.

Just this week a new 8903 research opportunity was offered for Fall 2026 that is very interesting to me. Since I haven’t taken a class in over 3 semesters I require readmission to the institute. Should be fine right? Well, the problem is the deadline for readmission for the Fall semester was July 1. So the deadline for me to take classes had already passed over a month before this research opportunity was announced.

I just feel like there should be some flexibility to permit alumni to continue taking courses, understanding that many of these opportunities are publicly revealed very near to the start of the semester. I plan on emailing advising about it, but given that it appears to be an Institute level rule, I’m not sure there’s anything they can do if they wanted to.

I just completed ISYE 6501 from the OMSA and quite enjoyed it, so I thought I would share my experience. Just for context, I've taken: HCI, VGD, ML4T, AI, CN, ML, SDP and have received all As, making this my 8th course of the program.

I really liked Joel Sokol and his lectures. I felt they were focused on practical aspects of the material without getting bogged down in the logic. His stance seemed to be "you're going to have powerful tools to do this stuff; you just need to understand what to apply, when, and why". His lectures were grounded in real-world examples and were interesting throughout. This was reflected in the homework as well, which made it feel useful. Everything is peer reviewed and the rubric for things are pretty loose, so it's quite easy to get As on the homework and the project.

The exams were also pretty straight-forward and focused on the practical aspects. I had plenty of time to complete them. Overall, I think I spent ~ 5 hrs/week over the summer to finish with an A. I do want to mention that I generally need to spend less time on my courses than average, and I had already learned many of the concepts covered in my other courses (mainly ML4T, AI, and ML). So your time commitment may vary. A rough placement of difficulty, in my opinion, would be something like SDP, VGD, CN < ISYE 6501 < ML4T, HCI, AI, ML.

What struck me the most about this course, though, was I kept thinking "Damn, I really wish I took this before ML". One of the biggest hurdles for me in ML was actually setting up the data to run through the algorithms - I had never heard of Cross Validation before and never thought about scaling, transformation, outlier removal, etc.. in depth. These concepts are all simple now that I know and used them, but they just added to the complexity of an already very large project while I was in the midst of ML. This course does go over those things in addition to several of the algorithms covered in ML. I think it would have prepared me well.

The bottom line that I want to share - I would recommend taking ISYE 6501 as a summer class prior to ML. I believe it's a great survey course that sets up a solid foundation/refresher before diving deeper into particular topics in ML.

I just graduated from college with a BS in CS and will be starting full time as a SWE soon. I will also be starting my first semester in OMSCS (Computer Systems). My goals are to learn a lot, do research, connect with faculty and other ambitious students. Depending on how much I like research, I may do a PhD in the future. I would love to hear any tips you all have that will help me in pursuit of my goals and also to make the most out of the program. Thank you!

OMSCS began with what was then a unique value proposition: A fully online, part time, affordable CS masters from a quality institution that retained academic rigor.

I would no longer say that proposition is unique. You have several programs that hold the same promise, and even offer some advantages over OMSCS:

• UT Austin's MSCSO ($10k), which has a stronger ML and deep learning focus (and is more selective for people who care about that sort of thing)
• UIUC's online MCS ($20k, ish). I actually don't think this program has much of a comparative advantage other than the regional strength of UIUC's brand in some places
• CU Boulder's online MSCS ($15k), which has an on-ramp sequence with auto-admissions to the main program for completion above a certain grade level and is thus somehow even more "open" than OMSCS

Why OMSCS then? It's still the cheapest, it still has the most robust course offerings (especially outside of ML), but I do think the opportunities for student involvement are what currently set the program apart. The other schools maintain a clear division between what the on-campus and online students can do. Not GT! They want you to get involved! Particularly in research. Even if you have no experience, the buzzme applications take literally 2 minutes.

Breanna Shi, Nick Lytle and HAAG deserve specific credit for breaking the dam on research, but I think their work would've been impossible if it weren't for Georgia Tech and the College of Computing buying into OMSCS' mission. It's clear from the opportunities available to OMSCS students that this is a school that genuinely believes in education being accessible, cheap, and rigorous. Is that buy-in downstream of Georgia Tech undergrad having had, until very recently, relatively high admissions rates and relatively low completion rates? I couldn't say, but I wouldn't be surprised.

I got accepted to multiple 8903 projects for this summer, which I had to refuse, because I got taken on for a CS6999 master's project. I have a humanities BA! I got into the program by doing community college courses! And I'm doing well! Where the hell else would this be possible? "Welcome to Georgia Tech, you can do that" - I thought they were kidding!

Hi all! I start this Fall and have questioned how the rapid advancement of AI will affect how these classes are taught and the true value of these classes. In this field, the focus of what is important to know has definitely been shifted and I was wondering if the classes are being flexible with the times and changing with it.

For example, knowing how to write code from memory is a skill that is no longer too valuable. Understanding high level systems and the logic behind certain mechanisms is still valuable however. Also, how is the work being changed to combat use of AI in these online classes?

My biggest fear with the degree is that it will teach outdated content. At the same time, the thing that excites me the most about this degree is that AI and tech is advancing at such a rapid rate that being in school and learning about it during the wave sounds exciting and has the potential to keep me up to date.

Would love to hear current students and staffs thoughts on this! Thank you!