Hi! I have two questions as I am starting this Fall.

1. So many courses look interesting! I am having a hard time picking only 10. After I complete 10, graduate, can I still take more? Or if I want to hold off graduation, can I take, say 11 or 12 and then graduate? Will they show up on my grad transcript?

2. I want to do ML but it's likely I will not get the courses I want in order I want. Some of these courses require me to take specific order so I grasp concepts better. Anyway, this creates a bit of a problem but in short: can I start this program with concentration in AI so that in the first year I meet the 2 course specs required? And then switch to ML in the middle of the program? Is there any restriction on how many times I can switch (just curious)?

I see a number of comments/complaints about ‘such and such class is useless for FAANG,’ or sentiments along those lines. I’m going into this program because I love CS and want a deep education in it. I figure that if I successfully fill that desire through OMSCS, it will likely lead to interesting CS-related work. Folks that do this just to get a FAANG job probably are less likely to stick with a program like this and get challenging roles.

Is there any truth to that, or am I just a hippie??

I am going to miss my second semester. In between family issues and attempting to start a company and switching jobs, I couldn't register for summer semester. First year, took three course all with good results.

What should I worry about? Should I contact an advisor?

I'm a little flustered since looking at the GA syllabus. Apparently the homework doesn't affect the grade AT ALL while still taking a lot of time and being mandatory?

Is this new or did they change it this semester? I found some very old posts indicating there was a time when homework counted towards the grade.

I’ve been in local slack groups in the past and was wondering if there is one for Northern Virginia? I know there is some coordination on an official level but not sure how to sign up or register.

From Fall 2026, the mandatory fees for taking 4+ credits has gone from $440 to $531. While the on-campus mandatory fee only increased by $10.

While checking the footnote, it leads to https://bursar.gatech.edu/student/tuition/2526_Fact_Sheet.pdf which mentions -

>Online Learning Fee

The Board of Regents (BOR) imposed the Online Learning Fee for students in online programs beginning in Fiscal Year 2025. Fully online students will be charged an online learning fee equivalent to the technology fee as well as 50% of the other mandatory fees.

This has two implications -

1. All other fees, which are technically not valid for Online students are still being applied to us, in some form. Yes, including the athletic fees which everyone protested and filled forms about.
2. THE CALCULATIONS DON'T MATCH UP FOR 2026-27!!
   • In 2024-25, the Online Fees is $440 which is nearly equal to $((748 - 129) * 0.5 + 129) = $438.50
   • But in 2025-26, the same Online Fees is $531, but calculates upto $((758 - 129) * 0.5 + 129) = $443.50 which is almost $87 lesser than what they are actually going to charge.

Either GT folks forgot basic maths, or they are fine with openly lying through their golden grinning teeth.

Reference links -

1. 2026-27 Fees Page
2. 2025-27 Fees Page
3. https://bursar.gatech.edu/student/tuition/2526_Fact_Sheet.pdf

BTW, in the PDFs, they also link https://www.budgets.gatech.edu/mResources/MSFAC which is a dead page. So apparently even the Technology fees isn't being to used to keep the docs updated properly.

2026-27 fees (left) vs 2025-26 fees (right). In On-campus the only change is $10 increment in Athletic Fees, but Online Learning fees increases by $91 !?

>Hello folks! We still have space for student in the new (2nd semester cohort) of the CS 8803 MGT & Leadership course. This is a free elective course 😄

About the course:
This course is designed to provide OMSCS students with hands-on organizational leadership experience through leadership roles in the Human-Augmented Analytics Group (HAAG). This course will involve working in teams as HAAG administrators and managing research projects. OMSCS students enrolled in the course will be assessed based on two components: their contribution to the management of research group operations and their work on a particular management initiative of their choosing. The initiative component allows students to implement and track the success of management techniques in a complex research environment, allowing OMSCS student teams to test innovative approaches in a sandbox-like, student-oriented environment. Students will be assessed based on oral and written reports reflecting their interactions within the team and the results of their initiative. The course is intended for any interested OMSCS student, regardless of their technical or management background.
You can check out the summer syllabus here: MGT & Leadership 8803

TL;DR: Brand new course (lectures freshly recorded Fall 2025) bridging classical graphics with the modern neural stack: NeRF, 3D Gaussian Splatting, PBD, and Diffusion. Generous grading (97% A rate among non-withdrawals), reasonable workload, and every assignment produces something visually cool you can actually show people. 9/10, strongly recommend if you have any interest in graphics, generative 3D, or physics simulation.

Background (for calibration)

Comfortable with Python and C-style syntax, advanced NN. Minimal CG experience.

What makes it stand out

Most OMSCS courses have lectures from 2017-ish that show their age. This one was recorded Fall 2025 and the reading list is almost entirely post-2020 papers (NeRF '20, 3DGS '23, score-based diffusion, etc.). It's the closest thing to a "current state of the field" survey OMSCS has right now for graphics + neural rendering.

Course content — 6 modules

1. Foundation of Graphics — ray tracing, Phong, GPU basics. Ramp-up for folks without CG background.
2. Signed Distance Fields — primitives, boolean ops, sphere marching, neural SDF.
3. Neural Radiance Fields — radiometry, volumetric rendering, NeRF, accelerated variants.
4. 3D Gaussian Splatting — splat math, footprint, 2D-GS, 3D-GS.
5. Position-Based Dynamics — particle sim, PBD, differentiable physics, neural physics.
6. Generative Models — diffusion, score/flow matching, geometry/video/world models.

Modules 2–4 are the heart of the course: how to replace the traditional grid/mesh pipeline with neural representations.

Grading

• Assignments: 64% (8% × 8)
• Midterm: 16% — 1 hour, open book, take it any time within a week. Prof gives review material in advance. Bottom 25% still got full marks (based on Canvas).
• Final Project: 16%
• Participation: 4%x

Grading is generous - 97% of non-withdrawals got an A (based on official GaTech catalog site).

Assignments (8 total)

GLSL shaders + PyTorch training notebooks. ~100–200 net LOC per assignment (excluding boilerplate).

• A1 — Ray Tracer: ray-sphere/box intersections, Phong, shadow rays, recursive reflection
• A2a — SDF Rendering: primitives, boolean ops, sphere marching
• A2b — Neural SDF: MLP fits mesh SDF using DeepSDF-style narrowband loss + clamp, render with ray marching
• A3a — Volumetric Rendering: Beer-Lambert, RTE, front-to-back discrete integration
• A3b — NeRF: positional encoding + MLP, train a tiny NeRF on lego/chair/drums/hotdog
• A4 — 2D Gaussian Splatting: parameterize 2D Gaussians, gradient-descent fit a target image
• A5 — PBD: position-based dynamics, constraint projection + Verlet integration
• A6 — Diffusion: forward noising, score matching, train a minimal generative model

Final Project

Two tracks: Creative (build a 3D scene, scored on artistry + tech) or Technical (mini research + report). I did Creative — built a Schwarzschild black hole renderer (geodesic integration + the A3a volumetric stack), took ~15 hours total. Reusing your own assignment scaffolding is encouraged.

Workload & difficulty

~10–15 hrs/week for an average student, spiking on assignment weeks.

Self-rated difficulty:

• 4–5 / 10 if you're comfortable with Python + C-style syntax and machine learning
• 7–8 / 10 otherwise (the math + shader combo is a real ramp)

Apple Silicon / integrated GPU is enough for the WebGL parts. Neural training runs on Google Colab.

Tips

• Save each assignment's fragment.glsl — you'll likely reuse one as your final project scaffold
• Skim the OG NeRF and 3DGS papers before those modules; lectures hit harder if you've seen the figures once
• Start thinking of your final project topic early.

The honest downside

The later assignments (roughly A4 onward — 3DGS, PBD, Diffusion) feel noticeably shallower than the earlier ones. Not really the course's fault — a faithful end-to-end implementation of any of those topics easily blows past the time and GPU hours you can reasonably ask of a single homework. You get the core math, a working minimal pipeline, and enough scaffolding to actually read the papers, but you're not training a full-resolution 3DGS scene or a serious diffusion model in your A6 notebook. Treat the back half of the course as well-scoped on-ramps for your own deep dives, not as production-grade implementations.

Bottom line

9/10. The most current course on the platform if you care about graphics, generative 3D, or simulation. Lenient grading and a real portfolio artifact at the end. If you're shopping for a Spring elective and any of these topics catch your eye, just take it.

Happy to answer questions in the comments.

So I've been lurking here for awhile trying to get a sense of what the program is calibrated at, and it feels like a ton of the posts I see are from people who don't have a traditional CS background. Folks who have some coding experience, but came into the field through non-traditional paths. Lurking in the subreddit, it makes it seem like those types of students are the majority.

Is that just an artifact of non-CS people being more likely to post in the subreddit asking questions about the program, or just more likely to mention their lack of a CS background while the CS majors assume that's the default?

I ask because I do have a CS background - did my undergrad in it, worked as a developer for many years, and now I teach at a tech school (DSA, linux system administration, software development). I'm wanting to do a Masters at some point to get to a point where I feel I can fill the shoes of some of my soon to retire colleagues who have their Masters degrees. My school isn't a research school, so I don't feel the need to do a traditional thesis based Masters and thus am looking at OMSCS. I just want to solidify my credentials for teaching undergrads.

I think in the end I'm trying to calibrate whether this program content, and its outside reputation, fills that niche or if it is seen more as a degree where non-CS people can get their first formal CS credential onto their resume?

Silly question, but the classes just started and I registered for a seminar but I cannot see the seminar in Canvas. Are the seminars also thaught in Canvas or is there another platform?

My current laptop is 6+ years old and has started experiencing significant lagging and heating issues. I am planning to upgrade soon, especially since I intend to specialize in ML/AI and will be taking courses in Machine Learning, Deep Learning, and Reinforcement Learning in the near future.

Even with my current workload, I face considerable performance issues while running tools like VirtualBox or VMs required for many OMSCS courses, which slows the system down substantially.

Since some OMSCS courses also have demanding computing requirements, I would appreciate recommendations from users based in India regarding suitable laptop options.

• Would a dedicated graphics card be beneficial for ML/AI coursework and projects?
• Is it worth opting for a Core i9 processor over a Core i7 for my use case?

My priority is to get a laptop that offers strong value for money and is worth the investment in the long run. I am also looking for a brand with reliable after-sales support in India, good service availability, and a relatively low failure rate.

Some of options I am considering:
1.lenovo-yoga-slim-7-ultra-9-32gb-1tb-ssd
2.lenovo-loq-intel-core-i7-13th-gen-13650hx-16-gb-512-gb-ssd-6 GB Graphics/NVIDIA GeForce RTX 4050)
3.hp-victus-intel-core-i7-12th-gen-16-gb-512-gb-ssd-windows-11-home-6-graphics-nvidia-geforce-rtx-4050-

Hello everyone,

I have a table reservation for Saturday, September 19th, 2026: the opening Saturday night of Munich Oktoberfest. (You may know these are notoriously difficult to get.)

I thought it could be fun to turn it into an OMSCS meetup and meet some fellow students, staff, or alumni in person.

I have 9 seats available. Each seat comes with a ~70€ food and drink voucher for the table reservation (roughly enough for 2 large beers and half a rotisserie chicken).

Before I organize anything further:

Is there an official place/process for OMSCS meetups?

Would people actually be interested in joining? I know this is a bit early.

I vaguely remember seeing a meetup megathread before, but I can’t seem to find it anymore. My apologies if this should be posted somewhere else.

I admitted to OMSCS for Fall 2026 and I am wondering if I can take class during the Summer 2026 semester. Had anyone done that before and How?

Hi! Does anyone know whether the following email is a phishing email or not? Email Address that sent it: ishaanvyas2004@gatech.edu

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© 2026 Georgia Tech

This feels like the kind of dilemma sage elder OMSCSers can have great advice on. As you know, courses are appearing in Canvas and populating our calendars. Here's how that shakes out for me:

I am signed up for RAIT. I know it's not considered an especially hard class, but it will be hard for me, since my only Python experience is Coursera and no, I have no technical background to fall back on. The professor has office hours at 8:30 on Tuesdays.

I am also signed up for the LLMs and Cog Sci Seminar. It's not the end of the world if I can't take it, but I'm obviously interested enough to pay for it, especially since I'm an educator and this seems ultra relevant to figuring out how to deal with my students' LLM addictions (and my own!). The seminar meets Tuesdays at 8.

I'm sort of assuming that means I should drop the seminar. But what do you guys think? I'm asking in part because this is my second semester in OMSCS, and my first semester class didn't have office hours of any kind, so I don't know how they work here. Is it mostly about asking your own questions, or listening to others questions? (In which case missing the first half hour each time seems like a problem.)

Non-CS background, bachelors in mechanical engineering and entering in the OMSCS program this fall. Folks in Canada/US who have come from a tech product manager pathway, has anyone had success with landing PM or PM adjacent roles in the SC space after doing OMSCS? If so, what roles did you target and succeed with?

Hi all!

I have seen many of the graduation posts and find them really helpful! So hope this one is helpful too.

0. Application

My journey of OMSCS begins in 2023. After I heard about this program (I really wish we can know this program earlier), I started to learn the edx courses about Java, data structure and algorithms. Then I applied for 2023 Fall and got an offer. However, during the verfication phase, I recieved another email saying my English test can not be waived. I checked the earliest time to take IELTS in my city and it was september so I was way passed the deadline.

But I still took the september IELTS english test and got 7.5 and then reapplied the programm again. By this time, I also finished the last part of algorithms. Thanks for my recommenders who are willing to write recommendation letter for me again! I got in again for 2024 Spring.

I would say these edx courses really built my foundation and my confidence. I should be more confident on myself but I didn't. I didn't know if I am able to do this program well or if I could get in. But I told myself if I can finish this, I can finish OMSCS.

1. Courses that I've taken

2024 Spring: ML4T. This is a perfect course for beginners like me. It helps us to understand what the workload is and get touch with ML which can be used in work later. I've heard that the exams have been changed to close books so I don't know how hard it is now. But back then, the questions were also super long and designed to confuse AI. This sometimes also cause confusion for non-native english speakers too. The TAs in this course are super helpful. They have live walk through for every assignment which is amazing!

2024 Summer: SDP. I am so lucky to have a group that everybody is great and hardworking. One of the groupmates is even a professional project manager who orgnized our meeting very well. We also corporate between different time zones. Near the end of the course, I moved to the US. I am grateful they also accomodate my traveling time.

2024 Fall: DL. This course was recommended by my groupmates in SDP. I learnt a ton! I like how they orgnize the assignments and I feel like I undertand more after coding them. I watched Andrew NG's deep learning videos as suppliment and it helped a lot. The group work for this course is also fabulous. We chose a topic ourselves and we repeated a paper and tried to beat it by different algorithms.

2025 Spring: NLP + simulation. I used the black Friday to get in NLP. NLP is one of my fav courses. The lectures are new and popular and easy to understand. After taking DL, this course become super easy.

I took simulation to refresh my probability knowledge. I really enjoyed the course materials and I spent more time on this than NLP. I remember carrying my laptop to finish the final essay in hotel lobby in Shanghai and the airport in san francisco.

2025 summer: CN + Nvidia AI agent seminar +CS6999

CN is something I planned to take to gain more foundation in computing system side. But I have to admit that it is super dry. I also watched videos online to help me understand better. The assignments are fun and I also carried laptop to Orlando and do homework on air and hotel.

Nvidia AI agent is a seminar that takes more time than a course but it is totallt worth it. DL and NLP really helped me to learn this seminar. I succeeded to get all the certificates in this seminar which I think they are really good deal.

CS6999: I read a news in linkedin that some students in this lab published paper and then I reached out to one of the students to ask how to get in. Then I applied by emailing the lab and sent my CV.

2025 Fall: KBAI + CS6999

KBAI : This is a easy course that I think I should've taken eariler. It is a good idea to take this as first course too and you won't be too excited about this if it is near the end of your journey. This course does not click to me as others. I feel the cognitive part is too abstract to me. Maybe I am a born engineer.

2026 Spring: AI +CS6999

AI: I saved this course as the last one because I think it is difficult. But it turns to be not as difficult as I thought. This course also overlaps with simulation, KBAI, ML4T in different parts. so I actually end up using less time even than CN. In this course, you can skip one assignment and 2 chanllenge quizes. I chose to skip the first assignment as I was busy at that time and also heard that it is the most difficult one. lol.

2. Campus tour and graduation ceremony

My advice would be go if you can. David Joyner really levels up the experience. After the campus tour, I feel more connected to the Uni. During the graduation ceremony, I feel like OMSCS is a more important and large community than I thought. The video camera is the in the straight direction to the back. (There are so many and I end up finding the wrong one.) The walk is super fast. David Joyner handed the diploma to the Dean and then Dean will shake your hand and hand you the diploma. I wanted to shake hands with David Joyner too. But I saw him handing the diploma so I did not because I don't want to disrupt the process, which I regreted a lot. For future gradutes, go shake hands and take selfies as you want. You will also have chance to take pictures with David Joyner in campus tour and after graduation ceremony which I took my chance. To be honest, I think he is more popular than Buzz.

3. Other thoughts

The classmates are amazing and you might feel left behind or not smart as others constantly. But we will survive. Learn from others and don't compare from others.

Thanks reddit community for valueble advices and posts!

See ya!

Go Jackets!