100 Most Recent Reviews

• B2R/DEtMZGuUBgdcFgzLTQ==2024-04-29T21:50:43Zspring 2023

  Introduction to Graduate Algorithms

  I took this class in Fall 2023 and Spring 2024. I will be passing on the 2nd attempt. Having taken this twice, here is my advice to do well on 1st attempt...

  First, understand the pace of the class. There's 3 segments of this class: 1) Dynamic Programming and Divide & Conquer 2) Graph Algorithms and RSA and 3) NPC Theory and Linear Programming. You do roughly 3 weeks of coursework, then the 4th week is exam week for each of the sections. You have HW and quizzes due every week with a couple of quizzes. That means the pace is fast and you have to stay on top of it while also watching lectures and reviewing the OH on a weekly basis.

  Second, understand the most important resources available to you and prioritize them accordingly. Not all course materials are equally important for your success. The most important is lecture videos and Rocko's OH. Use Rocko's OH to understand what the staff wants you to focus on most. Use the lecture videos as your primary source of content. The textbook is supplementary. Don't waste cycles studying items in the textbook that are not in the lecture. Use Joves' OH to do exam review. This is going to really help you during your exam week. Yes they are very long OH but a wise investment!

  Make sure to memorize and nail down the core algorithms from each section, especially if its covered in lecture. For DP, I found it crucial to memorize LIS, LCS and to be able to reproduce those from hand. For D&C, I found it crucial to practice the binary search practice problems over and over. For graphs, I found it critical to memorize the runtimes, common input/output transformations. For NPC, I found it necessary to memorize and understand SAT, independent set, vertex cover, clique problem-solving approaches. Your HW should guide you where to over-index your studies. If your HW involves a binary search technique, you should 100% practice as many of these as possible and do them cold! If your HW involves LIS, make sure to know LIS inside-out, draw out the tables quickly and make sure you nail the salient points in a correct solution.

  If you are short on time for exam preparation, focus on your free-response question preparation. In the worst case, you can make educated guesses on MCQs. I would also suggest combing through the regrade requests to see common mistakes, sample solutions and how your peers approach their problem-solving. I also recommend holding off on travel plans or anything like that, as they can be very disruptive in a fast-paced course. (On my end, I had a 1-week business trip and that completely destroyed my ability to catch up on the second module of the class so please don't do that as that adds unnecessary stress.)

  All in all, while this course does have scary reviews, note that the teaching staff is on your side and your peers are invested in succeeding too. You aren't alone and this is a really important class for improving your algorithms skills. Even though I had to take this class twice, and it was devastating to do so, I got over it. It made me more resilient and my algorithms skills improved. Whether you do this class once, twice or more, understand you CAN clear this hurdle with the right mindset and effort.

  Rating: 4 / 5Difficulty: 4 / 5Workload: 15 hours / week

• qwZ+Wb2rEv1JbO+JU64e/Q==2024-04-29T18:43:13Zspring 2023

  Machine Learning

  For context, I am an SDE at a large tech company working on traditional ML products.

  I found this class to be very good, and I think the changes TJ has made are positive. I took RL with him last year and enjoyed the class. Because I took RL I pretty much knew what the TAs were looking for and scored pretty well on most of the assignments. I found this class to be easier than RL, but more effort. Most of the time the stress was just from the fact that I had write 10 pages, not necessarily the content.

  I do get that it can be frustrating that the grading is pretty much just a laundry list of "did you talk about the thing in the rubric?" and less "is your paper interesting?", but the sooner you figure that out, the better. The right balance is to make sure you hit every point in the FAQ, and then try to make an interesting paper.

  If I was to approach this course now, I would skip the lectures, read the book, and use other sources to learn the material. Then, to study for the final, I would go to the lectures. The lectures are fine, but I think they require a bit of base knowledge for them to make sense. Some of the lectures made no sense till I read the corresponding chapters in the book.

  For the exam, I haven't gotten my grade back, but it felt very fair.

  I spend a lot of my day to day dealing with nebulous stuff, and being able to explain and justify things is pretty much a key part of being successful in the workforce. I think this class actually prepares you quite well for the soft skills that are required to be a ML engineer.

  Rating: 4 / 5Difficulty: 4 / 5Workload: 15 hours / week

• eclYOX8keMVl61sVIOSFEg==2024-04-29T15:44:53Zfall 2023

  Graduate Introduction to Operating Systems

  This class was a good first class, but I came in with decent C knowledge. I procrastinated on the final project (and I had no C++ experience), and it really bit me. I still ended up with an A, so I guess that goes to show that you can bomb a project and still be ok after the curve. Exams were easy in my opinion, but some people found them pretty difficult. My main piece of advice is to start projects early and use the resources recommended by TAs and other students in the Slack channel.

  Rating: 4 / 5Difficulty: 4 / 5Workload: 15 hours / week

• EU0V2hBOSQlHoOuW62L5vg==2024-04-29T07:17:48Zspring 2023

  Computer Networks

  I wanted to hate the fact that it is mostly a text based course, but the material provided is pretty high quality, so I don't mind. I also think this is the best medium for the professor as she's just not a very engaging presenter. Overall, the teaching staff did a good job running the course.

  I enjoyed some projects, others weren't as fun, but the hints on Ed from the TAs always pointed me in the right direction. If you complete all assignments and don't totally blow the exams its an easy A.

  I've heard people say this isn't a graduate level course. I disagree; being easy does not mean it's not graduate level. Sure, the first half of the course is the basics of networking, but the second half covers higher level topics, like efficiently utilizing CDNs and studying internet censorship. These are graduate level topics in my opinion.

  Overall, if you are systems track taking this class is a no-brainer it fulfills a core requirement without much difficulty and let's you relax a semester, or pair with something more time consuming. Overall good class, highly recommend.

  P.S. Took in Spring 2024

  Rating: 4 / 5Difficulty: 1 / 5Workload: 8 hours / week

• QaHiGrgd+Pjfq59R17SqTA==2024-04-29T03:56:30Zfall 2023

  Digital Marketing

  Too easy

  People are blatantly using ChatGPT 3.5 and copy and pasting output on their work.

  Why not just make it a full 100% test-based course? Of which you can keep the 30% midterm and 30% final, and introduce 40% of which are weekly proctored quizzes?

  And then organically give bonuses to people who truly deserve it by active participation in classes?

  MGMT classes are truly turning the OMS degree into a degree mill, indeed.

  Rating: 1 / 5Difficulty: 1 / 5Workload: 3 hours / week

• OTMORSBgU2cPEuwKJq+EwA==2024-04-28T20:56:06Zspring 2023

  Computer Networks

  This class is okay. Its biggest flaw are the lectures being extremely boring. I mostly am writing this review to comment on the workload. While I've taken other conceptually harder classes in OMSCS (GIOS, HPCA, GA, IHPC) I took longer than I expected based on the other reviews on the homeworks simply because I am pretty rusty in python. So if you're like me and you're rusty in python you may spend longer than the other reviews imply...

  Rating: 3 / 5Difficulty: 2 / 5Workload: 15 hours / week

• SSSOP28ZCXJKKGVI/KdIhg==2024-04-27T17:04:46Zspring 2023

  Distributed Computing

  This course is fantastic.

  It's a lot of work, but you're building a real distributed system from scratch.

  Projects are done in phases which each phase building off of the last. There are robust tests that you can run locally to help you implement your project. Many people in the class had confusion about how Java works and got frustrated about how state works. You'll be a good place if you understand how Java works and how the dslabs framework tests your implementation -- it's actually really cool though likely unfamiliar.

  The first three phases are fairly easy and are a good opportunity to score points. Front-load your work at the beginning of the semester so that you won't be as stressed about the last two phases which are very difficult.

  I gave up during the last phase which was a choice I was able to make because I had done well early on. From what I know, few people complete the last phase because of the difficulty. It's a fun challenge, but you need to know when to stop to manage your stress levels.

  The exam and other homeworks/lectures were useful and reasonably challenging. The projects are definitely the stars of this course.

  My key takeaways:

  • Front-load work at the beginning of the semester.
  • Manage your expectations and know when to stop. Don't burn yourself out.
  • You will probably not 100% complete phase 5. That's okay; refer to the above.
  • Spend time understanding the project setup, read the dslabs paper, get a better understanding of Java. It will pay dividends.

  Overall I would say this is one of the best courses I have ever taken. It is very challenging, but not impossible. IMO it's easier than compilers because this course focuses so much on the project vs compilers focusing on both the project and the theory.

  Rating: 5 / 5Difficulty: 4 / 5Workload: 30 hours / week

• SSSOP28ZCXJKKGVI/KdIhg==2024-04-27T16:56:44Zfall 2023

  Video Game Design and Programming

  This was easier than most of my undergrad CS classes.

  The course is more of an exercise in organization and project management than it is in making a video game.

  From day one create GitHub issues and a sprint board. Meet up every week and create/assign tasks. As long as everyone puts in a small amount of effort it's pretty easy to create a game that meets the expectations of the course.

  The milestone projects are fun and were where I did most of my learning in the course. Get good grades on these so that you can stress less about the team project.

  The lectures are absurdly long and drawn out, and have little relevance to your grade. They're useful at the beginning for understanding the milestones, but they are quickly irrelevant. I would skip most of the lectures and went straight to the quizzes.

  Rating: 2 / 5Difficulty: 1 / 5Workload: 5 hours / week

• SSSOP28ZCXJKKGVI/KdIhg==2024-04-27T16:52:49Zfall 2023

  Computer Networks

  This class was fairly easy. The homeworks and exams were fair and the workload was light. The labs can be frustrating because the documents are hard to understand and the course VM is strange, but they aren't intrinsically difficult.

  Rating: 3 / 5Difficulty: 2 / 5Workload: 8 hours / week

• nwVzYoTwu/jzK+O8v/Y2DA==2024-04-26T20:41:23Zspring 2023

  Machine Learning

  Review: My review of the course is the following "excellent but not perfect". The reason that it is excellent is because you don't measure how good a course like this is based on some of the irrelevant things you're seeing many people say below(stylistic criticisms of the lectures/layout etc). You judge how good this class is(and the overall ML program) based on what people are doing with it after. I had no ML experience prior to doing this program. I wrote my first line of code 2 years ago when I first took Udacity Intro to Python. Since taking this course, I got hired for my first role(not ML focused). But because I spent tons and tons and tons of time doing the extremely challenging assignments, problem sets, and exams while trying to get an A(which I did) then you will be able to succeed at getting an ML role which I was able to do at my company(really quickly after getting in). Not only that, I am running the show now. I am designing the entire company's prediction systems, RL systems for distribution, and even CV now for shelf inventory. Theres much more(unsupervised learning for basket analysis etc). The only reason I was able to do this successfully in the real world was because this class is actually RIGOROUS. Thats a very important word. It means something particular. Courses that aren't rigorous enough for engineering/STEM are not effective in the real world. People are not joking when they say this class and ML track in general are on par with Stanford, Berkeley, MIT. I would know because my undergrad was at Berkeley. It is definitely on par with the rigor(I flirted heavily with the tech world there even though I wasn't a CS major). Grading was more generous at OMSCS for the B threshold(not for the A) compared to Berkeley but overall still very challenging. So yes this class is extremely good. Charles and Micheal are actual GOATS in this field. I wasn't even born yet by the time Charles finished his triple major with a 4.0 at GT and was gearing up to start his PHD at MIT. These are actual beasts teaching the class. Their lectures are actually good(not saying they're the best) but they're for a different audience. Originally they were made in the context of Udacity(which is why its conversational in nature, which many ppl dont like cuz they think its distracting). I also think the conversational styled lectures are distracting but they fill the needs for marketing side(entertainment factor) at Udacity. Overall solid course and program. I dropped out of medical school to do this program and Im not even quite done yet but its changed my life. So thats my review. If you want to be an ML god just work really hard here. You dont even need to be from a CS background(I was neuro). I don't like to talk about money but I think its worth mentioning that for dropping out of a 70k a year MD program to do an online 7k program was scary. Lets just say that with whatever pay I am experiencing I have been able to pay off my parents house. Get my mom a new car. Its been a surreal experience doing GT. Don't give up hope if you haven't been hired yet. It takes a LOT of time(I experienced 100s of rejections). Especially in this current moment with everything going on in the world(Poverty, Disease, Climate Change, and much more).

  Tips for Success: The assignments are tough to finish so starting early is necessary. There are two components you need to focus on in order to succeed. The first is the empirical investigation component. You're actually conducting experiments on these projects. You need to set up valid experimental pipelines(validation, learning etc). The FAQs help but they don't really explain WHY we do things in the order we do them in and WHY that specific step. It needs to be intuitive as to what things serve as controls and why they created the FAQ list of tasks the way they did. All of that is what I call the empirical portion. Some of us are only from CS background and so that aspect of it might not be as intuitive(mine was in Biology with lots of research so I was better at this aspect). Part 2 is the mechanical portion. This is basically your actual analysis. Speaking to the structure of the data. The mathematical variables(limits, preference biases etc) which make one algorithm perform better than another. You also have to do more robust comparison because algorithms may be equal as far as accuracy but not time complexity. Why is it faster? Speak a little bit about the underlying mechanics of the algorithm making it faster and much more. This second part help you push yourself from that B range to A range. Its the depth of your analysis at every level(data, the actual algorithm itself etc). According to a TA last year, the primary factor in grading is the depth of analysis. So go deeper beyond just the FAQ. Follow the FAQ but its just a starting point. Go in more depth and you'll succeed. Most ppl aren't able to because of time so start early. Thats why I wrote that in the first sentence.

  P.S. Please excuse my grammar and writing. I know it could have been more concise. Goodluck!

  Rating: 5 / 5Difficulty: 5 / 5Workload: 40 hours / week

• WH5373cPLLpepSYVKiE4bQ==2024-04-26T16:52:15Zspring 2023

  Introduction to Graduate Algorithms

  If there's one thing I'd say about this class, it's don't be afraid. Don't come into this class apprehensive because of the reviews. I found the class to be well run overall, considering the class size. Deadlines and logistics are communicated in a clear and timely manner. The TAs are great. They genuinely care and want you to understand the material, and to succeed. They actively feedback to improve the course, so reviews from just a few semesters ago might not reflect the current course quality.

  Some advice for future students..

  The tests can be stressful, since they are worth 70% of your full grade. My #1 tip for tests is to deeply understand the HW problems, since the exams free response questions are adaptations of these. Be sure to understand exactly why you lost points on HW problems, so you correct those for the Exam. The MC questions are generally scattered trivia of the lectures, although some may be a bit more involved.

  Study group is not necessary - you get more than enough feedback by actively participating in HW / Exam problem threads on the Ed Forum, with TA guidance.

  Regarding practice problems, they give you everything that you need - you shouldn't need to look at anything outside of their weekly practice problems to succeed in this course.

  I recommend attending office hours, or skimming the recordings. It's a great resource, and I regret not attending more of them.

  Overall, don't stress out too much. There are many opportunities and resources to succeed in this class.

  Rating: 4 / 5Difficulty: 4 / 5Workload: 14 hours / week

• 4kluWNzA+TUcEksL17C2JA==2024-04-26T16:15:33Zfall 2023

  Introduction to Theory and Practice of Bayesian Statistics

  This is one of the best classes I've taken while in OMSCS having taken DL, HDDA, RL, ML, QC & NetSci so far. The other reviews are correct that the video lectures aren't great. They are ok up until about unit 4/5. Luckily Aaron, the best TA I've had in any undergraduate or graduate course I've taken, has made a website where he has re-done many of the lectures using PyMC which is a great resource. There is also no longer any need to use WinBUGS (also thanks to Aaron) you can now finish the course using only Python will little issue. To supplement the lectures I recommend getting a copy of Ben Lambert's book "A Student Guide to Bayesian Statistics" which is one of the best textbook I've ever read and watching the lectures Ben made to accompany the book on YouTube. Between Ben and Aaron's work you should be able to get ALOT out of this class. Make sure you know basic calculus and probability before taking the course as well. The later is covered in the lectures fairly well though at the start. Also be sure to go to Greg and Aaron's office hours if you're stuck on the homework!

  Rating: 5 / 5Difficulty: 4 / 5Workload: 10 hours / week

• 4kluWNzA+TUcEksL17C2JA==2024-04-26T16:06:34Zfall 2023

  Network Science: Methods and Applications

  The subject matter of the course is interesting and the quizzes and homework aren't too hard. The main issue I had with this course was 1) the lack of TA response on the Ed forums. Some TAs were really good about this but for the rest I wouldn't be surprised if they didn't reply to more than 5 post the whole semester. For example for quiz 12 or 13 I think all the questions on the discussion post were left unanswered until less than 12 hours of when the quiz was due! 2) The quiz grades are released a week after they are due even though they are auto-graded.

  Rating: 3 / 5Difficulty: 3 / 5Workload: 8 hours / week

• keaufVj7I4UoKSG8ZRFGlQ==2024-04-25T21:53:07Zspring 2023

  Machine Learning

  I have ~3 years of work experience with machine learning, but I had only been exposed to supervised learning before this class. My grades thus far are: A1 - 81, A2 - 87, A3 - 84. I expect A4 to be my lowest, but I should safely get an A with curve unless I skip the final or something.

  Yes, this class has a lot of ambiguity. No, it is not a bad class. The lectures are very helpful but the "humor" is incredibly annoying. Ed discussion / office hours will save you countless hours on assignments. This is more of a writing course, where one is expected to produce reports similar to research papers. If you can put together an organized paper that is relevant to the assignment then you will pass.

  I really wanted to add my own review for this class, because many other reviews here are too extreme in my opinion. I browsed these reviews for a while at the beginning of the program, dreading having to take this course in the future. This anxiety was completely wasted as I actually started to enjoy the course after I gave up my procrastination and jumped in. There is a lot of content expected in the reports, so it takes a while to get everything there but it is not particularly hard or anything. Use an Overleaf account with IEEE formatting. If you can write in English at the graduate level, you have nothing to be worried about. I feel this is a huge challenge for many in the program, hence the negative reactions. This course is definitely a marathon, but if you lean into the open-ended nature of the analysis you might even find yourself having a bit of fun while you learn tons.

  Rating: 4 / 5Difficulty: 4 / 5Workload: 20 hours / week

• 7uR36kqoo3zxtwjZAL2nsg==2024-04-25T19:25:19Zspring 2023

  Introduction to Graduate Algorithms

  Spring 2024. Tough (rather cruel) grading on the exams. Final exam (which can replace your lowest exam grade) is nothing like the HW's or previous exam questions (completely different topics). Some TA's are great (especially Rocko and Joves) but professor is completely absent (TA's really deserve professor salary). I had a great experience with OMSCS until this course. I got great training through taking advantage of the most difficult courses in computing systems specialization (my favorite was Distributed Computing)- the program helped me advance my career. Im grateful for that. But I ended this course with a C and it sabotaged my degree. Im too busy with my young family to retake- this class is really not worth it anyways. MY ADVICE- do Interactive Intelligence specialization to avoid this class and supplement your electives with great computing courses (like DC and operating systems) or other difficult courses.

  Rating: 1 / 5Difficulty: 4 / 5Workload: 23 hours / week

• lA48d7MtIulpHlQB/ZD46w==2024-04-25T02:37:59Zspring 2023

  Introduction to Computer Vision

  It's my 7th course and by far the hardest, however I really enjoyed the material and assignments - the lectures are fun and informative, and you will learn a ton from this course. The 3rd assignment (AR and Image Mosaic) was the most brutal one, I probably spent 80 hours on it.

  On the negative side, I was accused of plagiarism in this course, which I didn't do. They dropped the accusations after I presented my evidence, but I just wanted to warn everyone that even if you write the code 100% yourself you might still be accused of copying your solution because your Haar features functions look "similar" to someone else. The functions basically find indices of rows/columns and take slices of an image, how many unique creative solutions can there be lol. My advice is - make commits as often as you can, it will preserve your drafts and save you if you get in such a situation.

  Rating: 4 / 5Difficulty: 5 / 5Workload: 30 hours / week

• QDaEPgzTIAol7AYwjIA05A==2024-04-25T02:11:29Zspring 2023

  Machine Learning for Trading

  Pros:

  • Improved my Python skills tremendously, I was incredibly shocked with how much easier I was able to put stuff together at the end of the year.
  • Early assignments slowly build confidence and don't take as much time.
  • Video Lectures are best I've had in the program (only 3 courses), they teach stuff in a straightforward manner.

  Cons:

  • Joyner papers are kind of a pain in the butt with all the formatting, do not leave these for the day before. I think I got caught a few times up till 3-4am finishing them up.

  Rating: 4 / 5Difficulty: 4 / 5Workload: 13 hours / week

• QDaEPgzTIAol7AYwjIA05A==2024-04-25T02:06:56Zspring 2023

  Game Artificial Intelligence

  This course left a relatively bland taste in my mouth. I went into this course with high hopes from what I thought was an amazing course in Video Game Design. Initially, I thought this course would cover more modern ways of implementing sophisticated and in that regard was completely disappointed.Maybe it was my overall misunderstanding of what was going to be taught, but I didn't walk away from this course thinking it elevated me as a Game Developer in any meaningful way.

  As far as what was taught in this class, the project work pushed me to learn at times but overall fairly boring. Some of the course lectures might be the most boring content I've ever consumed in my lifetime.

  Rating: 2 / 5Difficulty: 3 / 5Workload: 13 hours / week

• 8h+7kJJ+/KOoBZn8aaQeFw==2024-04-23T03:32:48Zspring 2023

  Introduction to Graduate Algorithms

  Spring 2024. I work as a machine learning engineer, I do not needed this class at all to do my job, complete waste of time. I passed successfully the class and this was my last course.

  Mind reading of TAs class. Subjective class to whatever the TA wants that particular day and if you complain you get punished or just ignored. Rubric not share with students for obvious reasons, they don't want to be judged. As it happens with every monopoly (since there is no other option), the people on the power feels that they can rule the word. Georgia Tech needs to add another option for final class to show that nobody likes this class and show how bad the professors and TAs are.

  If you fail an exam, go ahead and do your best, do not feel that you are a bad student. It is just that you were not able to guess what they wanted.

  It is so bad that this was my last class, because I completed the program with a terrible feeling because of the GA experience. Pretty bad since all the other classes were awesome, like DL and RL, but I cannot express enough how bad this class is. A communication specialist could easily do its thesis with this people.

  Rating: 1 / 5Difficulty: 5 / 5Workload: 30 hours / week

• 8AxDzE3RxkCU+WRxTiSsqA==2024-04-20T15:11:50Zfall 2023

  Special Topics: Introduction to Computer Law

  This is a wonderful class that will definitely bring you practical insights for your next venture. On top of Digital Marketing, AI Ethics, these are the classes that bring real value to my business, instead of coding, coding, writing papers for the sake of doing it. It is more efficient if you take it in addition with another course during Fall or Spring. Why take those 3 easy classes alone during the summer? There are two projects: business acquisition and code analysis, don't fake them, do the research. Do it seriously, you will be fine. The lecture quizzes are fine too, as long as you watched them. They are well done.

  Rating: 5 / 5Difficulty: 2 / 5Workload: 3 hours / week

• FjephUxAUzok5zFb+qpSmw==2024-04-19T19:36:36Zspring 2023

  Introduction to Graduate Algorithms

  I took this class in Spring 2024 as my last OMSCS class. I finished with >90% and didn't take the final.

  Background

  Non-CS undergrad degree, working as a software engineer. I have a lot of Leetcode-style algorithms experience and some proof-based math experience (undergrad level analysis and algebra).

  This is a medium-difficulty algorithms class that can be very challenging for students without enough exposure to mathematical reasoning.

  Quizzes

  These are nearly free points. Most of them allow multiple attempts. If you watch the lectures you can easily finish the quiz in 10-30 minutes. I averaged 98%.

  Coding projects

  Pretty fun and free points. The median grade on every coding project was 10/10. Other students will usually write exhaustive test cases which you should definitely run against your code.

  Homework

  These are where you have to really learn the material and style of solution expected by the class. Highly recommend having an active study group so you can critique each other's solutions. Read the example solutions very closely and write your solutions with mathematical precision. I credit my study group for getting a 90% average on the homework.

  Exams

  The questions themselves are not difficult and are slight variations on the homework. I could come up with the basic approach in a few minutes. The points I lost were because of insufficient precision in my answers. I recommend spending any free time on the exam closely analyzing every step of your solution. I can see the exams being very difficult if you have exam-related anxiety. I think my experience answering coding questions during tech interviews eliminated this. My exam grades were 91%, 81%, 100%.

  Study advice

  For those with experience with proof-based math, you should treat this class just like a proof-based math class. I think a lot of students took too much of a plug-and-play style to learning this material which sort of works for exam 1 and 2 but not at all for exam 3. In the regrades for exam 3 (NP-completeness) it was evident that many students still didn't know how a reduction works or what its purpose is. You should go through the lectures slowly and understand the purpose of every step.

  Rating: 5 / 5Difficulty: 3 / 5Workload: 10 hours / week

• QaHiGrgd+Pjfq59R17SqTA==2024-04-18T13:52:34Zfall 2023

  Special Topics: Financial Modeling

  This class should be renamed Financial Modeling for Insurance Agents instead.

  Even an 8-year old kid who dreams to be in the insurance industry can come to take this class and score an A.

  Rating: 1 / 5Difficulty: 1 / 5Workload: 1 hours / week

• e0KUjdqAVcl35UfRdTIymQ==2024-04-18T03:04:12Zfall 2023

  Introduction to Graduate Algorithms

  I came into this with little algorithms background. I am non CS. I took a low rigor data structures course at a community college 3 years ago.

  I attempted it once and didn't pay this class much time/attention, making it my lowest priority, and dropped after Exam 1 with an average of 69.5%. I came back and made it my top priority and finished all 3 Exams at the top quartile and made an A.

  Practice, practice, practice. Even if you feel like you understand it, practice some more. Do/review the same problems multiple times. I also benefited a lot from solving the foundational D&C algorithms like FFT, Binary Search, and Median of Medians in homework format, although this isn't something people commonly did or something they recommend, I just did it and it helped a lot.

  In terms of learning, I now am able to solve a good portion of Dynamic Programming and Graph problems, and am in general a little bit better at solving Leetcode problems. I feel like I have a pretty solid understanding of NP-Completeness. I'm also a lot better at handling algorithms formally.

  Rating: 5 / 5Difficulty: 4 / 5Workload: 12 hours / week

• H3JCxR1prTMaSAtsMwjjmA==2024-04-17T23:29:11Zfall 2023

  Introduction to Graduate Algorithms

  I took this in Spring 2024 as my last OMSCS course and ended up with a high A (97%) About me: CS undergrad, poor at math, work full-time as a Software Engineer at a demanding big tech company

  Contrary to what other reviews may suggest, this class is very well run. The TA's are very responsive and genuinely want students to succeed. The class has a very rigid weekly schedule and this is a good thing. Grading and exam difficulty were extremely fair. The TAs provide a comprehensive write up on answer format for each type of questions. I did not ever need to submit a regrade request and I noticed most of the regrade requests were from folks not understanding the question or the expected answer format. All exam questions were extremely similar to HW problems. The math heavy sections seem daunting but the lectures do a good job of dumbing it down to a non-math major and TAs are very helpful in clarifying anything that the lectures might miss.

  Things I did:

  • Followed exactly what the schedule suggested (watching lectures, HW and suggested practice problems)
  • Focused mainly on video lectures (as opposed to the text book)
  • Attended every office hours
  • Read and understood the expected answer format guidelines provided by TAs
  • Joined a study group (We didn't meet but used discord chat to discuss HWs)

  Things I didn't do:

  • Jump ahead of schedule
  • Skip HW practice problems
  • Solve more problems than what was suggested
  • Focus too much on the suggested text book content

  Overall, the key is to consistently follow the schedule and truly listen to/read the guidelines provided by TAs and internalize them.

  This worked for me and I hope it works for some of you achieve the grade you desire

  Rating: 5 / 5Difficulty: 3 / 5Workload: 15 hours / week

• 5B+OBPZbth9LTScRvJn1cQ==2024-04-17T23:09:34Zfall 2023

  Knowledge-Based AI

  This was my first course in OMSCS.

  Honestly a really well-structured course, it becomes very apparent that they have been considerate of different learning styles and different viewpoints. Lectures are impressively well-put together. Instructor and TAs were really accessible and responsive. Content was pretty easy to digest, and complexity of assignments were very fair (sometimes too easy).

  However...terribly time consuming. Almost weekly, we had to do some coding assignment, write a paper about it, and do some peer reviews on top of that. While it's still quite an easy course, sometimes it feels like there's very little time for a break, so work ahead whenever possible!

  Rating: 4 / 5Difficulty: 3 / 5Workload: 20 hours / week

• 7RqJJOi7Q/l7bB59mDCwgg==2024-04-17T23:03:48Zspring 2023

  Introduction to Graduate Algorithms

  This review is for Spring 2024, although OMSCentral won’t let me select that as an option.

  Background: I took GA as my 8th course and first algorithms class ever having a non-CS undergrad. I worked full-time while taking it, which was difficult and put a lot of strain on my personal life and health due to the time commitment (I only took maybe one weekend off the entire semester). I am ending the course with a hard-fought B, which is also my first B in OMSCS. But I’m okay with that, and I’m looking forward to graduating and getting more sleep. I think I owe a massive thank you to the Head TAs, who are all top notch for this class. They seriously go above and beyond to help you out on Ed and through Office Hours. I really enjoyed their guidance, and while Vigoda’s lectures are decent, I wouldn’t have made it through the course without their support. I had nothing but highly positive experiences with the Head TAs. I also took Prof. Brito’s Language of Proofs Seminar, and would comment that Dr. Brito also seems to be a really kind and funny person.

  General Thoughts: My exam grades (out of 60) were as follows: 57, 39, 40. I found the pacing of the course to be pretty fast right up until Exam 3. The TAs have made an effort to improve the course’s chronology over time so that you’re better prepared for exams with homework feedback. So now for example you’ll get feedback on a D&C homework before Exam 1, which didn’t always used to be the case. I overall felt the course was fast and difficult, but the exam questions felt mostly fair to me if you’ve done the homeworks and attended the office hours. That being said, I think the weightage of the exams is a huge source of student stress. There are 3 exams (or 4 choose 3 exams for Spring/Fall) with 2 free responses each, meaning a single FRQ is worth 7.7% of your final grade (for comparison, all the homeworks combined are worth 14%). I agree with the teaching staff that this makes the homeworks a more forgiving experience where you can make mistakes and learn from them without severe penalties. However having 46 points of your final grade come from 6 questions is still pretty stressful if you need the course to graduate. I wouldn’t double up this class unless the other course is < 5 hours/week. I do want to stress that the exams were all fair. There are some remarks in the reviews below about formatting and having to put in regrade requests on a public forum in Ed so that your peers can give feedback first. Personally I don't see how the class scales successfully without these measures, and whenever I genuinely needed a regrade, my peers and the TAs came through for me. If your solution is correct, I would stress that you'll get the points you deserve (eventually).

  Recommendations: I did well on Exam 1 by attending all the office hours (Rocko is amazing at leading these, and Joves’ marathon ones are also incredibly valuable) and practicing and re-practicing all the homeworks, lecture quizzes, and recommended practice problems. I do NOT think you need to do all the practice problems for every exam although I’m sure it would help, focus on the homeworks and intuition for why the model solution for the homework is correct. On Exam 2 I messed up a free-response, and I really didn’t want to take the cumulative final (due to feeling burnt out) so I mentally checked out and settled for a “good-enough” score for Exam 3. Rewatching all the lectures just prior to an exam was helpful for consistently getting the multiple choice questions correct.

  Closing Remarks: I’ll save constructive feedback for CIOS. My biggest non-constructive gripe is that GA is not required for all specializations. If GATech thinks the ML spec needs to take it, I don’t see why that doesn’t apply to HCI and II as well. I’d encourage you to take GA because it covers interesting topics with great support from the teaching staff, and I’d definitely say it’s made me a better programmer / computer scientist. I was pretty scared of the class when I started due to student reviews (and I mean to be fair, it is my first B in the program). But I have no prior algorithms course exposure and again I don’t have a CS undergrad. This course was pretty doable (in my humble opinion), but you gotta buckle down and really focus for a few months. As I look ahead to graduation, I'm really thankful for this program and what it's able to offer working professionals who don't have the time or financial ability to do a full-time on-campus Master's. Pay it forward if you can, and I hope to see you on the other side!

  Rating: 4 / 5Difficulty: 5 / 5Workload: 20 hours / week

• vnUwlFbMAi6sAwxmV6O4jQ==2024-04-17T22:52:07Zfall 2023

  Introduction to Graduate Algorithms

  I'm writing this review on the day I got my exam 3 score back. I got through this class with an B and I couldn't be happier. I'm going to try my best to give a nuanced perspective. Let's start off by saying that this class is tricky to review. Exams make up 69% and the remaining 31% consists of hws, quizzes, and coding projects. The material itself is not too difficult and I'd say that the exams are a fair assessment of what we did with homeworks and practice problems. That being said, I had a tough time with this class. Not a tough time with the concepts but with the ensuing stress regarding whether I'd pass due to small slipups in the exam.

  Because this is an exam heavy class, making small mistakes in any exam can have a huge impact. This was the source of most of my stress. I did not do well on the first exam. Completely bombed it because of a really dumb mistake on my part. In a different moment, under a different setting, I felt I could confidently solve the problem. And for the rest of the semester I was under a ton of pressure to make the B. My situation with the first exam probably differentiates my experience with those who did well from the get go. There are probably people who did well early on and kept that up throughout the semester. They probably had an easy time with the class and got a high B or an A. There are also people like myself who probably struggled due to a slip up in the exam. The difference in our reviews will be the stress levels we endured and I'm sure that reflects in the variety of reviews you'll be seeing.

  My advice to you is to take this as a standalone class. It is definetly far more doable. I took this class alongside another one and I'm pretty sure that didn't help. With the extra time you have, make sure to really really understand the hw problems. Do your best on the hws, quizzes, and coding projects. I'm sure those points are what helped me get a B. In addition, practice the DPV problems and read the textbook if time permits. Exams consist of 2 FRQs and an MC section. Doing well on the MC section requires a solid understanding of the material. FRQs are based on hw and DPV (textbook) problems. Pay special attention to your hws. I wish you the best of luck with this class.

  Rating: 4 / 5Difficulty: 4 / 5Workload: 10 hours / week

• qz+q7i99Y0g0xnMY7Zwx4A==2024-04-17T16:30:13Zspring 2023

  Special Topics: Systems Design for Cloud Computing

  Despite the review being labeled for 2023, I took this course for the Spring 2024 semester. Just wanted to submit this before I forget.

  The topics covered in SDCC are easy to grasp (relative to AOS topics) and the workshops are somewhat useful for exploring what you've learned... but the project work is tough. Really tough. We started with 85 students and had 54 remaining after the withdrawal deadline—nearly 40% of the class. Ultimately, though, I feel the course and its lessons were very insightful and worth doing. This is a chance to learn the foundational concepts that the "cloud" is built on; hugely beneficial if you're someone that is already working on either building infrastructure in a public cloud, involved with designing cloud-like infrastructure for an organization, or writing applications that will be eventually deployed in the cloud.

  If you are not already familiar with systems programming or cloud concepts, I'd strongly advise against pairing this course with another—particularly if you have family, full-time work, or a social life and/or hobbies you want to keep up with. You WILL need ample time to read into these topics and various docs to be successful in the course and gain the most out of the course. If you enroll with a mindset of "whatever I'll just grind through the code and GPT the assignments", you are missing the point and wasting your time—even if you pass. If you're not working full-time and are already somewhat familiar with the course topics, pairing with one other course should be fine.

  Some other qualities I took note of during the semester:

  The Good:

  • Prof Kishore is very enthusiastic and eager to talk about and expand on topics
  • Subject matter is interesting and relevant—unlike a large portion of AOS, I can see how the tools and course content might be applied in practical scenarios (like at my job)
  • The projects are rigorous but offer many learning opportunities
  • I appreciate that the course allows for—and even encourages—collaboration and chatting about potential solutions; a fair number of students were active within the Slack channel throughout the term.

  The Bad:

  • Some of the project specifications and resources are vague, outdated, or incorrect.
  • While I understand the intent, I don't think the weekly sessions should be mandatory for passing the class. People in other time zones (sometimes, other countries) are greatly inconvenienced by this and the sessions themselves don't really provide any value—other than students having the option to asks questions live.
  • Considering that CS6210—the prerequisite to this course—already has a MapReduce project, it is somewhat disappointing that the same topic is being re-hashed... even if it is a fuller implementation of the framework. Surely there are other interesting innovations in systems software that we could study and implement?! Folks who are auditing or are somehow in this course without taking CS6210 course might be fine with this... but I wish we could have explored something else.
    • Also somewhat annoyed with the fragmented approach. Rather than splitting MapReduce into workshops with odd requirements, it'd be better if we were simply assigned the entire project and given 3-4 weeks to complete it.
  • Scheduling for workshop and project grading is kind of rough. TA's share out a (poorly-formatted) spreadsheet and tell teams to sign up for times to present their workshops or projects. There are either not enough TAs to support the number of teams or TAs don't have wide enough availability windows to accomodate everyone.
    • In addition to this, TAs may not post their availability until the day before they are available and may—albeit, rarely—change their availability. This is pretty inconvenient at times, especially considering we are not automatically extended the same grace unless there's some major, documented event (e.g., outage of GATech Github, medical reason, (provable) personal emergency)... at least, that's the tone that's set from the beginning of the course.

  The Ugly:

  • TAs are basically MIA on Ed and Slack. Maybe they're only answering private posts?
    • Would be nice if they were responsive beyond office hours or the weekly meeting; I understand that this is likely a part-time gig for them but the least they could do is check Ed and respond to a few posts. And even that was barely done.

  Rating: 4 / 5Difficulty: 4 / 5Workload: 15 hours / week

• ntdlxje+NLk4rRE2nVpOiQ==2024-04-16T01:04:50Zspring 2023

  Software Development Process

  Worst course of all time. Total waste of energy and time to almost send myself to a psychologist therapy session every week. There's a stupid Assignment 6 that consists of 15% of your grade and the average is 68% (I got 43%), which got me fighting for my life to get a B for the last few weeks. The material learned is totally irrelevant to my professional career. Also doesn't help when this course is forcing to use Java which is probably the worst language ever created.

  Rating: 1 / 5Difficulty: 4 / 5Workload: 25 hours / week

• szFGSy34W3OiXNupCVHubw==2024-04-15T06:50:44Zfall 2023

  Computer Networks

  I took this course in spring 2024. There are only 4 assaignments + 2 exams for this course. The content is related with how CPU or cache works. how it quckly stores data, read data and why it works so fast....

  The grading for exams will be very good.e.g. If your part1 fails, it will affect your grading on part2. That is my very appretiate.

  The course is more about the experiments and theortical answers. But each assignment's last part is about coding on C++. You would be better if you know some basic knowledge about the C++ coding otherwise will be more difficult.

  The worst part about the coding is it doesnot have IDE. you only code on textbook. very sad for beginers. No easy way to debug the code. But good news is all coding line amended will be less than 100 lines. so no much code need to be added.

  exam will much more focus on the lectures. not hard but need to watch all lecture videos and practise the sample exams.

  Rating: 4 / 5Difficulty: 4 / 5Workload: 19 hours / week

• R2heKw+FhNUo9VKLa/P0Qg==2024-04-15T04:45:42Zspring 2023

  Machine Learning for Trading

  Not very difficult or overall time consuming, but the course content ended up feeling surface-level and borderline pseudoscientific.

  The projects are specified to fairly exhausting detail. If you're new to python this structure is nice, but otherwise it felt like jumping through arbitrary hoops to get full credit.

  AI4R, ML, RL, and AI cover this content in more depth without nickel and diming you on the rubric.

  Unless you're interested in trading specifically, or want a lot of direction for projects, I don't think ML4T is worth the time.

  Rating: 2 / 5Difficulty: 3 / 5Workload: 12 hours / week

• nxSQb6FOcVHHJT2ueblSNQ==2024-04-11T10:38:51Zfall 2023

  Special Topics: Quantum Computing

  The lectures are very nice and informative, but these do not match the assignments and grading, someone already said "Lectures are all purely theoretical and assignments are all purely application based", I couldn't agree more... This is a very chaotic course. I regret havign taken it, instead of taking some MOOC (as KAIST/Coursera's Introduction to Quantum Information, Chicago/edX's Quantum Computing for Everyone Professional Certificate, or just IBM Quantum Learning).

  Maybe for physics graduates is good (I have seen some reviews), but I was feeling pretty lost most of times, a terrible lack of guidance on the assignments, I was coding these, even trying to do unit tests that actually passed, but, once in the server, these did not work, and the feedback was pretty useless, "true is not false" and stuff like that: no way to test, no way to know what you could be doing wrong... When I had already decided to withdraw, I even checked on the Web for blogs of other people doing the same with the same framework, but here things did not work. As mentioned, I ended up frustrated and withdrawing. I do not recommend this course, just watch the online videos (once they are public in GaTech's Kaltura), and some MOOCs/IBM QL.

  Rating: 2 / 5Difficulty: 5 / 5Workload: 15 hours / week

• Lk7eFEPKT/Uqqqc2fkRxtQ==2024-04-10T14:28:49Zspring 2023

  AI, Ethics, and Society

  This class is tied for third place as my favorite class in the program, including heavy hitters like AI/ML/etc. It is not a programming class, and it will have little utility for someone in a low-level individual contributor role. Instead it touches on issues that are likely to be hit as a team lead or manager in any regulated field, and more broadly any software that affects the public.

  The course is fundamentally structured around US law. Professional Engineers have a legal obligation to consider the health and welfare of the public, and while most CS grads won't get a PE license it is still important for management and team leadership to consider the impact of work on society - even if you don't personally care about ethical behavior, the legal ramifications of getting these things wrong could land you and/or your employer in jeopardy.

  Having taken engineering ethics at an engineering school, this is a well-designed computer science equivalent: an ethics class that introduces you to the legal and ethical issues that you are likely to face while you are developing computing, AI, ML solutions. I have run into some of these in real life, and it will be nice to have the legal background as a buffer / correction to my general inclination.

  I enjoyed the lectures. I did not necessarily love the work in this class, but it was graded easily and didn't take all that much time, so as a way of keeping me on track it did what it needed to do.

  Rating: 5 / 5Difficulty: 1 / 5Workload: 8 hours / week

• pknyIxt/vvnKbg0cMSyv0A==2024-04-05T14:12:53Zfall 2023

  Embedded Systems Optimization

  All of the course content is outdated by a decade. Lectures are just bad.

  Professor is completely disengaged. Only showed up to one or two office hours for 10 minutes.

  You learn almost entirely about compiler optimization techniques that are outdated. Very minimal embedded computing content.

  The only practical things I learned in this course were computer architecture based.

  The head TA Darrell is the only good thing about the course.

  There are much better courses in the OMS curriculum that give you real world skills. Stay away from this one.

  Rating: 1 / 5Difficulty: 3 / 5Workload: 12 hours / week

• hrpccSqZKPxK8pOvrvLNHw==2024-04-05T03:03:24Zspring 2023

  Software Architecture and Design

  This is the first time I've critiqued a course mid-semester because the administrators of this course made me so angry. I have an anxiety disorder myself, but my symptoms were exacerbated when I saw the ED Discussion discussion these past few times. The mismanagement of the course team has resulted in students being docked points that they don't see as their fault. This is not my experience alone, there are dozens of students expressing their displeasure underneath a large number of posts. It's now the end of term and the course team have made 3 major mistakes. And with student feedback not doing any good, I guess the only way to express my dissatisfaction is by scoring the lowest possible grade here.

  Rating: 1 / 5Difficulty: 2 / 5Workload: 10 hours / week

• v9NUIlfgPsIu3SbY1R9d3A==2024-04-05T02:27:24Zspring 2023

  Machine Learning

  Spring 2024 Review:

  I took this course as my first one in OMSCS since I am pursuing the ML specialization. For me this was a make or break course in terms of staying or leaving in the spec, so that explains why I took it as my first course even though I knew the supposed horror stories.

  The reviews here from semesters that TJ LaGrow started giving the course are fairly accurate. So I'll divide it into sections that most people point out:

  1. Lectures: This is spot on in the fact that the lectures are somewhat outdated. The lectures are the same batch of videos that Dr. Isbell and Dr. Littman made ages ago. The lectures are either extremely confusing, boring and tedious (Isbell taught lectures) or straight to the point and actually digestible and understandable (Littman taught lectures). The one thing they have in common is the excessive use of jokes and puns which can get distracting to an annoying extent most of the time. Sometimes Isbell goes on insane tangents that don't amount to something useful so you can just skip that if you wish. Overall the lectures were not horrible, but they were not great either. They will help you get a proof of concept for you to be able to do the assignments well. This is especially accurate if you have an Intro to AI course under your belt, not necessarily the GT one (I didn't take intro to AI at GT, rather as an undergrad in my alma mater). One thing I do recommend is to stay ahead by at least one lecture according to the schedule in order for you to be able to finish your assignments in time and well (e.g. if this week's lecture is SL2, then you should already have watched SL2 and are in the process of watching SL3 or SL 4). Staying ahed of schedule somewhat is KEY to be able to have breathing room for assignments.

  P.S. Don't feel stupid af if you can't do the quizzes, they are annoying and you probably won't be able to solve them. It got to a point where I just watched the solution and learned from the solution that they explained instead of attempting them and wasting time

  P.S. The book is dog water, don't even bother. I read the first couple of chapters, but the lectures are a recap of the book (at least at the beginning, it gets to a point where they don't even reference the book since it's so outdated so, don' even bother using it)

  2. Office Hours: This is an acquired taste imho. Me personally, I went to like 1-2 overall even though they cordially force you to go. To me it was just a bunch of people asking the same stupid questions again and again, and some people would hog up the entire hour following up to the same stupid question they asked originally, and tangents are a common thing. Also, most of the questions asked don't even pertain to your assignment since everyone does their paper extremely different. I started to do better when I stopped overthinking what I did not do that Johnny boy did according to what he asked in OH. TL;DR ignore OH in general, focus on your own analysis and your own work, trust me. The FAQ in Ed actually says a to of what the TAs want in the papers.

  3. Assignments: These are time consuming, but rewarding at the end. The hardest one is the first one since you'll get slapped in the face with the fact that this ain't a typical undergrad CS course where you make your code so well it speaks for itself and you spit out a garbage report. No, these papers are expected to be well written, well-analyzed, more in the sense of "This model performed this way because of X, Y, Z reasons". Think about bias, variance, trade-offs, and most importantly plots, plot,s plots, and oh did I forget? PLOTS. Visualizing your data in plots is the most useful and underrated thing you can do. Not one TA cares if you're a one-liner python god because this is not that typer of course. What they care is that you can analyze and explain your experiments with the help of plots and metrics to tell a compelling "story" of how you got those results, why did you get them, can they be improved, how can they, etc. They may seem useless and that they don't teach anything if you don't make an extensive analysis like I mentioned.

  In terms of the paper itself, LaTeX and Chat GPT-4 are your best friend. GT offers premium overleaf subscriptions that you can use the IEEE conference template to make your papers. A1-2 are limited to 10 pages (including references), A3-4 are limited to 8 pages (including references) and LaTex helps a lot in organizing plots in correct sizes so you can add a decent amount of plots while still having a well written and concise report. Use GPT-4 to generate plots from your code, as well as re-write some paragraphs you may have already written. You can also let GPT-4 help you analyze the results you got and derive conclusions yourself from the help it gave you. DON'T copy straight up what GPT-4 says, don't cheat yourself out of a learning experience. The bulk of the learning here is done by the reports, trust me on that. Isbell and his stupid sarcastic puns in the lectures won't teach you anything compared to the investigation you do yourself in the reports.

  In terms of code, stick with Python all the way through, don't switch to Java halfway. Use scikit-learn (and mlrose-hiive, which is a wrapper of scikit-learn made for GT's ML A2 specifically) for A1-3 and don't overthink the dataset selection, it ain't that deep. Just choose a simple one that might have compelling ML things (large num of features, class imbalance, etc.) as well as an interesting application in the real world since these will be used from A1 to A3. For A4 just write your own MDPs in Python (with GPT help of course), bettermdptools is horrible and so is Gym.

  4. Final Exam: Spring 2024 had a difference to previous semesters in that they removed the midterm and only kept the final. The final is the same as others have said, Multiple Choice-Multiple Answer and it is proctored using ProctorTrack. Just study the flash cards and recaps and you'll be fine (especially if you did well on the assignments).

  5. Extras: There is a reading/writing quiz worth 5% of the grade that has unlimited tries. It's to help you understand how to write a paper basically. There is also a problem set, which is a mix of Isabell's old PS and questions taken from past final exams. It's not graded but if you turn it in solved (even if it's incorrect) they will take it into consideration if you are on the border of a grade at the end

  TL;DR Stay ahead, use the assingnemnts to learn, show and put in the effort and you'll pass with at least a B, good luck!

  Rating: 4 / 5Difficulty: 4 / 5Workload: 30 hours / week

• tWoDXZoAjQ9qXJlFiIBG/Q==2024-04-05T01:19:34Zfall 2023

  Deep Learning

  This course's design is like somewhat survey class and the instructor wants to cram all things at once in a single video.

  You have to look for other alternatives online to better understanding what the lecture wants to teach you.

  Rating: 2 / 5Difficulty: 5 / 5Workload: 15 hours / week

• tWoDXZoAjQ9qXJlFiIBG/Q==2024-04-05T01:16:56Zfall 2023

  Machine Learning for Trading

  Nice course. Not so much to learn but indeed a good intro to ML and nice application to trading. The projects are designed fair enough and the notes provided are in detail and very useful for exams. Suggest you take another course at the same time.

  Rating: 5 / 5Difficulty: 2 / 5Workload: 5 hours / week

• B9ODZY/BT5HrZA2E/KF9WQ==2024-04-04T01:44:31Zspring 2023

  Internet and Public Policy

  I took this class because I wanted a lighter semester but I didn't want to delay graduation. I am in the OMSCS program doing the ML specialization.

  Honestly, I was pleasantly surprised at how nice this class was. I wrote down 2 hours a week of work but it was more like you'll spend multiple weeks not doing much and then just sit down and hammer out whatever assignment was given.

  The assignments were reasonable and relevant. The projects (it depends on your group, but mine was solid) are straight-forward and feasible. If you are not a confident programmer and you don't have a confident programmer in your group, I could see the phishing project being an issue. I am a senior SDE with a CS undergrad so it was more fun than anything.

  There are some assignments that you have unlimited time to do and honestly just watch the lectures to be able to complete them. They won't ask a question verbatim as in the lecture, so you have to actually understand what the lecture said.

  The vibe of the course is chill but I was surprised at how both dry, yet interesting the content was. An allusion to this would be watching the show "How It's Made". It's kind of dry to watch a couch being made but at the same time you're like "oh that's how they do that". Lol.

  Also, useful info is there are no tests. There is a research essay at the end that took maybe the weekend, but overall your weekends will be pretty open for this course.

  My final note is perhaps the most important: There is no unreasonable bullshit busywork. You don't have to graph some stupid shit for no reason or talk about some useless thing in a giant essay just to fill the course. The assignments are reasonable and straight-forward and I really appreciate that they made the course as straight-forward as possible to learn the material.

  Rating: 4 / 5Difficulty: 1 / 5Workload: 2 hours / week

• YLpUziZEu588BAs1Hna4bw==2024-04-03T18:38:50Zfall 2023

  Computer Networks

  The course is relatively basic, and homework is due once every two weeks, which is relatively easy. The explanatory materials for all assignments are clear and TAs are supportive and nice. The only problem I think is the lecture content is rather boring.

  Rating: 4 / 5Difficulty: 2 / 5Workload: 8 hours / week

• vXVdSdTLx9SqaING9vcP6g==2024-04-03T03:07:51Zfall 2023

  Big Data Analytics for Healthcare

  The course syllabus have changed and Scala and Hadoop was removed, and now the course is manageable below 10 hours per week.

  However, I felt like I didn't learnt much, there's some simple spark processing and a really cool kaggle competition, but most of the content is simply some data wrangling with spark / numpy / pandas.

  The project on healthcare paper reproduction is also interesting. Choose a paper that is not difficult with Github written with clean code and you should do fine.

  Overall, I felt I've learnt more about Machine Learning than Big Data.

  Rating: 3 / 5Difficulty: 3 / 5Workload: 8 hours / week

• ML40KueKrVVBvrtJ6q4vAg==2024-03-31T14:03:17Zfall 2023

  Mobile and Ubiquitous Computing

  Really horrible course with a ton of potential. Rather than teaching or making projects/exercise that are cohesive our build, they simply took an inperson class and shoved it into an online format. Out of 10 classes I've taken, this is by far the worse in terms of content and learning. Take the class for an easy A and to check a box. Otherwise, prepared to be disappointed.

  Rating: 1 / 5Difficulty: 1 / 5Workload: 20 hours / week

• FI2nrW7+VsXdUQgLu1QNEg==2024-03-30T17:28:15Zfall 2023

  Mobile and Ubiquitous Computing

  this course is not the best,

  Rating: 1 / 5Difficulty: 4 / 5Workload: 40 hours / week

• n70rALB3Re1J2WuKho9B4A==2024-03-29T19:45:59Zfall 2023

  AI, Ethics, and Society

  A very dreadful class. Not much to teach or learn, and the homework is extremely tedious and repetitive. The content and assignments really need an overhaul.

  Rating: 1 / 5Difficulty: 1 / 5Workload: 7 hours / week

• B9ODZY/BT5HrZA2E/KF9WQ==2024-03-29T18:43:20Zfall 2023

  AI, Ethics, and Society

  I voted for Joe Biden in the 2020 election and this class made me decide to vote for Donald Trump this November.

  Rating: 1 / 5Difficulty: 1 / 5Workload: 7 hours / week

• Zipr8wSl9cgM/YJwGrdmYg==2024-03-29T16:47:27Zfall 2023

  Human-Computer Interaction

  Interesting material and great lectures. Scored a very high A in the class. Joyner has since redesigned the course, and as of Spring 2024, it reportedly requires half as much writing but more punctual reading for closed-book quizzes. I'll talk about what has not been changed:

  For written deliverables:

  1. Read the questions early.
  2. Watch the lectures early after reading the questions so you know what you need to focus on in the lectures.
  3. Start the assignments early and write to the rubric. It is available on Canvas. DO NOT DEVIATE FROM THE RUBRIC. You do not need to come up with ideal examples in your responses, and you can even make up designs and/or evaluation metrics. The class is largely concerned about giving you experience with the process, which means that as long as you can show that you are properly engaging with the design process, then you are good to go.

  For Peer Feedbacks, do them until surveys are released. Once surveys drop, just spam the surveys until you make up the rest of your points. Then you'll be freed from doing peer reviews for the rest of the course.

  Rating: 4 / 5Difficulty: 2 / 5Workload: 10 hours / week

• GVOAUZmYRwF+E367OR4ehA==2024-03-29T14:46:18Zfall 2023

  Graduate Introduction to Operating Systems

  This was my 7th course in OMSCS. I do not have a CS background or undergrad.

  I have to admit this course threw me off at first. I was very confident going in because I have a knack for learning new programming languages very quickly and excelling at them - I'm sure many of you can relate. I have 4.0 in the program and have taken many other difficult courses without too much struggle. I thought this one would be the same - especially given the "intro" title.

  For me, I spent more time on this class than any other. If you do not know C programming, you will struggle at the beginning. Fluency in modern languages like Java or Python will not completely translate to you quickly understanding C.

  For the non-CS background people reading this, when you program in C, you really need to understand how memory is allocated on the stack and the heap - many modern languages handle this for you. If you don't have a good grasp of memory allocation, I strongly suggest you search YouTube for videos explaining stack vs heap and what parts of your program are allocated to each.

  Project 1 was very hard for me. For the first time in my life, I was facing down an 8 am deadline the night before with code that came nowhere close to passing the auto-grader. I wish I had taken all the advice and learned C programming in advanced.

  Because I was struggling and frustrated, I initially hated the content of this class. But as the course continued, and my grasp of the C language improved, the course got easier. I ended up getting an A thanks to the curve.

  Looking back in hindsight after the frustration has passed - the content of this course is really good. This class covers a considerable amount of an undergrad CS degree. So if you are like me and feel like a "CS imposter," this class will fix that.

  In my job I get pulled into interviews for developer positions, and I have started to ask interview questions derived from this course because I now understand how critical these fundamental topics are to success as a developer.

  Rating: 4 / 5Difficulty: 4 / 5Workload: 25 hours / week

• zPMpZ4qbv+TFymmd8hVL2g==2024-03-28T12:35:48Zsummer 2023

  Data Analytics in Business

  This was one of the easiest class...With that being said, I see this has a lot of low rating, but I am not so sure why. Some parts of the lesson does suck. Now that I read the reviews, I vaguely remember that marketing section exists...But I think the real take away for this class for me was how to use R.

  This class shows you line by line code on how to use R for analysis, and I learned a ton about R that introduction to modelling did not teach...other than that, I do not think there is much to this course.

  Just take the exams and do your project and you will end up with an A.

  Rating: 4 / 5Difficulty: 2 / 5Workload: 5 hours / week

• zPMpZ4qbv+TFymmd8hVL2g==2024-03-28T12:31:05Zspring 2023

  Introduction to Analytics Modeling

  How to succeed: Listen and UNDERSTAND all the lesson materials. Try your best on the HW and review the answers later. This class is not a hard class, but as the name suggests, just an introduction.

  Overall, I do not feel like I wasted time, but I do not feel like I gained a lot. But I am still giving it 5 points because I got things out of the course that is exactly aligned with the name. introduction to modelling.

  I wish this class was an elective for the A track guys. As I am C track, I found this course not so relevant for my future, but who knows.

  Rating: 5 / 5Difficulty: 3 / 5Workload: 9 hours / week

• zPMpZ4qbv+TFymmd8hVL2g==2024-03-28T12:26:46Zspring 2023

  Special Topics: Business Fundamentals for Analytics

  I think this class should be an elective for the business track guys. A lot of memorizations to be done with great attention to details. The test questions are confusing. I ended up with a C, but I know for sure if I studied A is definitely possible (I am not saying this just to make myself feel better. I took DL, CDA, DO, etc and this is the only classes I have a C in. All the others I got A's...). Just take this class as one semester of annoyance and you will end up with an A. Have fun.

  Rating: 2 / 5Difficulty: 3 / 5Workload: 8 hours / week

• zPMpZ4qbv+TFymmd8hVL2g==2024-03-28T12:22:24Zspring 2023

  Computing for Data Analysis: Methods and Tools

  How to succeed: Do all the homework and mock exams that they give you. Really try to understand everything that you do on the HW by searching stackoverflow, etc.

  Great introduction to a master's class. Definitely a foundation stone for the future classes you will be taking.

  I thought homework are interesting and great examples of real-life application (maybe except for the string manipulation). Yes, the strict grading programming can be little bit of pain, but they really ensure fair and quick way to make sure that you have the right answer.

  I think this course is a good measure of how you will perform in this program. It challenges your coding and logical thinking area.

  Rating: 5 / 5Difficulty: 3 / 5Workload: 10 hours / week

• zPMpZ4qbv+TFymmd8hVL2g==2024-03-28T12:17:33Zspring 2023

  Data and Visual Analytics

  How to succeed: Know javscript for D3. Know SQL. Know PySpark. Then you will be good to go!

  This class is not really related to visualization. Rather, they put together a bunch of technology that is relevant for data engineering/high end data scientists.

  If you want to get something out of this class, when they are doing AWS/GCP/Databricks/SQL, with the extra time that you have study those 4 in deeeeep depth. Those 4 will be very relevant after you graduate. Other things they put emphasis on such as D3, final HW which is little bit of classical machine learning and a final project, DO NOT WASTE TIME ON THOSE. For classical machine learning, just take computational data analytics class (it is an awesome class BTW).

  Anyways, Overall this class focuses on all the wrong things and flies over the important ones. I would just spend this semester looking at the above 4 things and ignore other things. Yes you might end up with a C, but you will be much more marketable than someone who got an A and knows how to code D3.

  Rating: 2 / 5Difficulty: 3 / 5Workload: 15 hours / week

• zPMpZ4qbv+TFymmd8hVL2g==2024-03-28T12:11:03Zfall 2023

  Deterministic Optimization

  Difficulty: If you are good at math (especially linear algebra), this course will be pretty easy. I spent about 10hr/week, but I can def see why others would spend a lot more. Tests are very tricky. So learn your concepts during HW well. the HW grades are "curved" since they are student peer graded. I ended up with an A, but barely.

  How to do well: Like I said, HW are "curved" but the tests are not. Really make sure you learn the concepts from the solutions published after the HW given. Also studying the previous tests help, which they publish fir studying materials.

  What did I learn and how was it: I did learn quite a bit. Given a situation that is deterministic, you basically learn how to model and optimize the system in various settings. The lectures and the HW are little dry, but honestly, as long as I learned in depth, that is all I cared about.

  Complaint: As other people say... HW to exam proportion is wayyyyy out of what they are supposed to be. Make sure that the HW's are TA graded, so that they are not "curved". Also HW is where the real learning happens, so students should spend a lot of time on HW (which is not the case since they are "curved") and increase the HW weights on the final grade.

  Rating: 4 / 5Difficulty: 4 / 5Workload: 10 hours / week

• aX7v+yTogLvsY6OhYBAN7A==2024-03-26T23:39:41Zspring 2023

  Game Artificial Intelligence

  Overall, it can be a good course if you are interested in gaming. If you don't, and you happen to be entering a busy period, don't take it because it will make you feel like doing something utterly tedious and useless for 70% of the time

  Rating: 1 / 5Difficulty: 3 / 5Workload: 10 hours / week

• +hHhhpWGBBONLUJ32JdYrQ==2024-03-24T03:42:56Zfall 2023

  Machine Learning

  This course at Gatech has got to be one of the worst. I can't understand why it's made mandatory, but I absolutely dread every minute of it. It's managed to sap all the enthusiasm I had for ML.

  Lectures: Honestly, most of us skip the videos altogether. We resort to YouTube or Google for explanations because the lectures are just painfully drawn out. Professor Isbell tends to go on and on, making it hard to stay engaged.

  TAs: It feels like most of them are just there for show. Their office hours are a joke. They often contradict themselves from week to week. Plus, their lack of attention to detail, whether it's organizing course materials or posting updates, is frustrating. It's like night and day compared to Joyner's courses.

  Assignments: These aren't about learning; they're about jumping through hoops to get the results they want. Understanding the requirements or grading rubric feels like trying to solve a mystery every time. It's all busywork, and the end product is just a report filled with nonsense. Your coding skills hardly matter; it's all about that damn report, focusing on making the visuals impressive and filling it with unnecessary fluff.

  Bottom line: Stay away from this course. If I could turn back time, I'd never have signed up for it. It's a surefire way to kill any interest you have in ML (or even OMSCS). Trust me, the other courses are a much more interesting compared to this nightmare.

  Rating: 1 / 5Difficulty: 5 / 5Workload: 35 hours / week

• jsmRaN0UA8iPxGKeKcZ47Q==2024-03-22T16:06:55Zspring 2023

  Introduction to Graduate Algorithms

  This course is the worst in the entire OMSCS program (and I did 6400!). AVOID AT ALL COSTS. If it is required for your specialization, reconsider the program or the entire institution! I mean go elsewhere than GeorgiaTech for your Masters. The lectures are outdated, the professor is absent, and the TAs crew is condescending and has know-it-all attitude. This course will leave you tramautized and questioning your life choices.

  Rating: 1 / 5Difficulty: 3 / 5Workload: 20 hours / week

• Kl6wd6HHkWJU/zR8wBs9Ww==2024-03-17T13:34:46Zfall 2023

  AI, Ethics, and Society

  Ok. This class has merit. A lot of people here hate on this class, but it has a great purpose. It is an excellent introduction to AI, Stats, and Data Analysis using Python. If you've not done any of these before, it's a great chance to learn. A lot of the criticism seems to come from right wing leaning people who are afraid the course is 'woke'. I don't think that's fair. Statistically, this class is showing when AI is biased and when it is not. Certainly many of the datasets we analyzed were not biased, and some were. If you want to approach subjects like race in an intelligent way, this class will give you the tools. The fact that some of the datasets don't show any bias or even any statisctical pattern leads to my one critique. It can be frustrating to compute figures that are meaningless. Or graph outcomes that don't follow any pattern. Do not let that discourage you. Just report the results and let them be. That's how real data works. You're not going to get a bad grade because you don't find bias. Sometimes it's not there. The assignments are sometimes a little ambiguous, but the TAs will clarify in Ed Discussion. I'm sure the TAs hate answering the same questions over again, and the course could benefit from updating the assignments a little bit.

  Rating: 4 / 5Difficulty: 2 / 5Workload: 4 hours / week

• DdWQS12tsZ78dqc1ajCb8g==2024-03-16T18:34:51Zfall 2023

  Natural Language Processing

  This is my 7th course and by far the best. It does a good balance of theory/lecture, programming assignments and paper overview. It doesn't beat you to death with sadistic assignments and provides a shell where you focus on the conncepts learned and see it working. I finally understood the intuition of a Transformer model and its variants. Prof. Riedl is stupendous and i wish he finished the entire modules.

  He should come up with advanced NLP since there is lot of interest on how to quantize and FineTune a LLM models using HITL RLHF or DPO. I think Finetuning itseld can be a course starting with multiple sub-word, PE and attention techniques that can be explored.

  Only thing that they can improve is better homework recitation by TA.

  Rating: 5 / 5Difficulty: 3 / 5Workload: 10 hours / week

• 0JpqZRdZ+5ISBWCVg9Dttw==2024-03-13T00:59:06Zfall 2023

  Artificial Intelligence

  The topics discussed in the course are very interesting. The projects do take time but you learn a lot.

  Rating: 5 / 5Difficulty: 4 / 5Workload: 30 hours / week

• Fx764/8uQ176zln00wiLog==2024-03-11T17:54:07Zfall 2023

  Digital Marketing

  I took this course along with Cognitive Science and Global Entrprenuership, and still finished this course within 3 weeks (thats right, the entire semester of work finished within 3 weeks even with two other classes going on). There are weekly discussion posts (easy peasy), a handful of short papers (easy peasy) and two exams (moderately easy). Finished with an A and was able to focus on my other classes. Content is very simple and stuff you already know if you pay attention to marking campaigns you see online. A 9th grader could make an A in this class, but I'm not mad about it.

  Rating: 3 / 5Difficulty: 1 / 5Workload: 1 hours / week

• KuOIUWmjjnFUwPKzd4UMoQ==2024-03-11T05:50:02Zfall 2023

  Artificial Intelligence

  This is the course that convinced me that reviewers on this site tend to give higher quality ratings to classes that are more difficult regardless of whether they actually deserve high scores. This academic version of Stockholm Syndrome is the only way a subpar course like this can get a 4+ rating here on OMSCentral. It's not quite as bad as the trainwreck that is GA, but at a 4+ it's so galactically overrated that it boggles the mind.

  • The workload is just too high. I don't think it's a problem to an absurd degree, but 3-credit classes should generally strive to stay under 15 hours per week of work, or certainly at least under 20. The ~25hr/wk workload is currently the 6th highest of courses reviewed from OMSCS. I'm a pretty average student and this estimate feels correct to me, but I skipped most of the extra credit and other challenge problems. Most assignments feel 20-40% longer than they should be.

  • The workload wouldn't be as bad if it was spent actually learning things, but WAY too much time is spent dealing with weird edge cases that aren't essential to understanding the algorithms. Edge cases happen in the real world, but there they can be easily debugged. The Ed discussion forums for this class were an unfortuantely essential resource since it's where other students would reverse-engineer the jank of the assignment. Ed discussion forums should be for additional help, not "the other half of the assignment instructions we just didn't tell you".

  • A very common and frustrating occurrence was when I'd pass all local tests, but fail on the Gradescope submission. I always felt completely helpless when this happened, as Gradescope was a black box that said little more than "hmm that's not the answer I was expecting". I would run through my code step by step confirming everything worked locally, but it still wouldn't pass. With non-descriptive errors, I could never be sure if Gradescope was failing due to a genuine bug on my end, some janky edge case, or an issue with Gradescope itself (which happened several times throughout the semester). Something like 25%-40% of all Ed posts were "everything works locally, but GS fails", so the problem was obviously widespread. We really need better error messages so we can at least attempt to resolve issues ourselves.

  • The TAs did not seem on the ball in Ed discussions, often replying late and with incoherent answers that were indicative of not understanding either the question being asked, the content of the assignment, or both. It was typically up to other students to get an actual answer to problems being faced, which was obviously not very reliable since it depended solely on the comraderie of others facing the same issue. I saw many questions that just went completely unanswered for 48+ hours. You want to start assignments early to account for this, but not too early so others hopefully have a chance to deal with the weird edge cases before you spend hours dealing with them yourself.

  • Ambgiuous assignment directions. It started OK with first few assignments, but context gradually declined. In some of the later assignments you're forced to implement functions with little more than a one sentence docstring. It was pretty common for me to have to work backwards from the unit test provided to figure out what was even being asked for.

  • Lots of math equations that are just theorem dumping with no examples or broader context to explain what's happening. There's very little enlightenment in much of the textbook and supplemental reading. Far too often I'd be implementing an algorithm and I'd come across a math symbol that I had absolutely no clue what it was referring to. Also, some like |E| would typically refer to the absolute value of E, but then sometimes refer to the determinant of E. Untangling this stuff can lead to more frustrating hours of debugging.

  • The lectures are very handwavy and don't even begin to prepare you for the assignments or the exams.

  • Exams have almost nothing in common with the lectures or assignments, and are very all-or-nothing. I recommend getting a digital copy of the book so you can ctrl+f

  I got an A in this course, but despite the high grade I actually learned surprisingly little.

  Rating: 2 / 5Difficulty: 5 / 5Workload: 25 hours / week

• G3KVZGHWA4G3FwdOj9e/Tg==2024-03-10T23:48:41Zfall 2023

  Human-Computer Interaction

  I dropped this class Spring of 2024 (at the time of this writing, the latest I could choose was Fall of 2023), my fifth class in the program so far. I am putting this review here for those like me that took based off of reviews from previous semesters; this class is no longer a lighter elective level, it is a core class and should be treated with that assumption of difficulty and workload going in. The class instruction and content is still okay, but the revamped workload is absolutely brutal, I do hope it levels out because this can be a good class, I do feel that the intent to make this a “difficult, serious class” because of complaints went too far.

  Rating: 3 / 5Difficulty: 5 / 5Workload: 30 hours / week

• 2u0jxIDIVgLE3vfs00X60A==2024-03-10T22:45:48Zfall 2023

  Secure Computer Systems

  For context, my day job is doing computer security research. Pros: The course covers a wide range of topics. For example, we talked about data privacy and k-anonymity, which I didn't know much about before this class. Cons: Some (maybe half?) stuff felt completely theoretical and irrelevant to the real world. I spent a lot of time memorizing syntax and theoretical frameworks. Some of the mechanisms described were so theoretical I have no idea how they would be implemented in the real world. The choice of things to focus on was bizarre. For example, the Authentication module had no mention of public key cryptography (in fact, the course barely mentioned it at all). The entire Authentication method was about an obscure paper about hardening passwords with keystroke timing. Which is a cute idea I guess, but not commonly used. And since there was no mention of asymmetric crypto, a lot of students probably left that class thinking passwords are the only way. The quizzes and tests were very ambiguous and wanted pretty specific answers. A lot of mind reading involved. (Lots of short answer as well) Lectures were very dry. Projects were tedious.

  Ultimately, I still got an A. But this class wasn't worth it. I cannot recommend it to anyone. Either you're already familiar with this stuff, in which case you won't learn much of value. Or you're new to it, and you'll be led down a rabbit hole of irrelevancy.

  Rating: 2 / 5Difficulty: 3 / 5Workload: 8 hours / week

• PkimOgKOFN4UDCSlxgdXZw==2024-03-10T02:47:09Zspring 2023

  Machine Learning for Trading

  • Content is easy to understand.
  • Programming assignments are easy to understand (I had prior Python/Numpy/Pandas knowledge).
  • Reports require Joyner format, which made me anxious about potentially messing up my formatting.
  • Assignments take forever to grade, so you don't have any bearing on your report style/formatting until nearly halfway through the course.
  • Quizzes were open book but were a poor test of knowledge and were often more of an egg hunt for "meta" course content.

  Rating: 3 / 5Difficulty: 3 / 5Workload: 10 hours / week

• QaHiGrgd+Pjfq59R17SqTA==2024-03-07T09:36:47Zfall 2023

  Human-Computer Interaction

  It is a core course of a spec now rather than just a pure elective course. Keep that in mind,

  With the addition of HCI specialization and the complaints that students who select HCI path will great without the rigor of an OMSCS course, the boss has decided to up its difficulty so past reviews are not a good indicator for this specific course.

  Rating: 5 / 5Difficulty: 5 / 5Workload: 26 hours / week

• yDLjYZSnL0WZX+N59lDC+A==2024-03-04T19:19:16Zfall 2023

  Introduction to Graduate Algorithms

  The class is not difficult to study as such but, its the pattern of evaluation. This feels more of an english class rather than an algorithms class. The homework assignments require you to describe the algorithm in words and then its a word play after that. The grading is highly inconsistent. Its basically I as a TA think that this should be your grade. Rubric is unknown and it suffice to say that it has been an extremely frustrating experience. If you give a different answer than what the TA is expecting, expect a major penalty.

  Rating: 1 / 5Difficulty: 2 / 5Workload: 15 hours / week

• f5LbeClAhhqYmXvJMHwplw==2024-03-04T06:15:32Zspring 2023

  Computer Networks

  Fairly easy course. Programming assignments due every 2-3 weeks, quizzes every week, and 2 exams.

  Rating: 4 / 5Difficulty: 2 / 5Workload: 5 hours / week

• f5LbeClAhhqYmXvJMHwplw==2024-03-04T06:13:31Zspring 2023

  Software Development Process

  Amazing course if you are already a software engineer Super easy A No exams or quizzes

  Rating: 5 / 5Difficulty: 1 / 5Workload: 1 hours / week

• f5LbeClAhhqYmXvJMHwplw==2024-03-04T06:11:54Zspring 2023

  Human-Computer Interaction

  Course is for non-software engineers 6-8 page paper every week Annoying group project

  Rating: 1 / 5Difficulty: 3 / 5Workload: 12 hours / week

• hywNQQpgkkaGNA7MIF86mg==2024-03-03T06:25:19Zfall 2023

  Introduction to Computer Vision

  Generally a pretty solid course What is good: good lecture recording, detailed walk through to fundamentals, linear algebra heavy and very beneficial. Demanding assignment to keep you engaged What is bad: Not up to date, one of the example is in one of the final project where it is trying to use CNN for recognizing door number but restricted use of YOLO for reason of "it will be too easy". This model is already the state of the art model so what in any meaning is the use of a final project that correlates nothing to the modern technology set. Heavy lecture video and very short time it left to finish the assignment. I think most of us are working, I spent Mon-Fri night trying to finish all video, each time roughly 2hrs only to find out that I need to finish a major assignment that due on Monday. On average, this leads almost 25-30 hrs of workload to my life and I literally have no time for self care, my wife is in another state, visiting her during the weekend and my schedule is hell for the following several weeks. Conclusion: worth taking, you won't regret it, but definitely a hell of a burden added to your life.

  Rating: 4 / 5Difficulty: 5 / 5Workload: 25 hours / week

• PjAwzX5S8rzvqOGfY0JbHQ==2024-03-02T19:28:34Zspring 2023

  Introduction to Computer Vision

  Such an interesting topic ruined by the worst possible set of assignments. The entire course feels like it is designed to ONLY test you to satisfy some huge egos rather than help you learn anything.

  The staff is irresponsible and could not care less if you learn as long as you're able to pass Gradescope somehow. This is my 6th course and by far the worst and the only disappointing learning experience. Highly recommend staying clear of this one but do learn CV by yourself (Prof. Aaron's lectures are great) or another course outside GaTech.

  Rating: 1 / 5Difficulty: 5 / 5Workload: 40 hours / week

• n70rALB3Re1J2WuKho9B4A==2024-02-28T17:00:33Zfall 2023

  AI, Ethics, and Society

  My last and worst class in OMSCS. The class discusses misleading graphs, protected class and bias, which could be covered in less than a week at its depth. Instead this is a semester long course, so tons of repetitions just to fill up the time.

  This class could've easily won the most tedious assignments award in my entire academic life. The assignments all exhibit an awful pattern of high volume of low value work. For example, in one of the so-called AI/ML assignments, students are asked to calculate mean and standard deviation for a dataset, copy that into a report and format, calculate those for 10% of data, calculate those for 60% of data. Repeat for a protected class, and repeat for all subgroups in the protected class. The calculation in Python is effortless, the time is spent on copying and formatting ~30 sets of values in the report. No one checks the result as long as some numbers are there. Data analysis is minimal, as long as you wrote two sentences.

  It's surprising that this class at its current state is considered a graduate level CS course and one of the electives for II specialization. Anyone with basic coding experience, keep your sanity and stay away.

  Rating: 1 / 5Difficulty: 1 / 5Workload: 6 hours / week

• FxufW8teIQblLVszOZOWiA==2024-02-26T08:51:06Zspring 2023

  Introduction to Computer Vision

  I share the sentiments expressed in the previous reviews for the Fall 2021 term. -" WE ARE HERE TO LEARN, NOT HERE TO AMUSE THOSE IRRESPECTFUL IRRESPONSIBLE WORST DAMN TAS. "

  My background: I took computer vision courses previously, and had conducted related research in this field. I am not an expert in Computer vision, but I had indeed published peer reviewed paper in this field.

  My reviews: Our primary focus should be on learning, and unfortunately, there were significant challenges with the handling of the course by certain TAs. This has been my seventh course, following successful completion of six others with As. However, this time, the experience has been notably unfavorable.

  The communication with TAs was inadequate, as questions posed on Eddiscussion went unanswered. Additionally, I encountered issues with grading accuracy. Despite providing correct answers, corrections were refused, and instead, I experienced insulting remarks and unwarranted teasing. It was disheartening to feel disrespected and undermined in my pursuit of knowledge. Most ridiculously, our score were simply deducted for the questions that were NOT even asked. - gradings were arbitrary, if you challenged them, they would threaten you through something like academic violation!!! They are not open, once they graded, even it was wrong, you had no choice but to accept, this was actually against academic integrity - but they do not care. What they (these so-called TAs) took advantage of the authority to make you fail the course.

  After six As in my previous courses, this is my 7th course. I had the WORST experience. First, TAs did not answer your questions, many (if not all) questions were unanswered in Eddiscussion. Second, TA graded your answer obviously incorrectly but refused to get it corrected. Instead, they insulted you, and found whatever comments they could to tease you - I felt being teased like a monkey!!!

  Rating: 1 / 5Difficulty: 1 / 5Workload: 29 hours / week

• 4kluWNzA+TUcEksL17C2JA==2024-02-18T00:48:41Zfall 2023

  Special Topics: Quantum Computing

  Good intro to QC from the CS perspective. Lectures are informative and homework and labs help reinforce the material. The one issue I had is that sometimes the problems in these assignments were not written very well and so were either confusing or outright incoherent. I suspect this will be fixed as more iterations of the course occur. Overall would still recommend despite this however.

  Rating: 4 / 5Difficulty: 4 / 5Workload: 15 hours / week

• XF7CqI5QZr6qy67/zIwMMQ==2024-02-12T20:03:39Zfall 2023

  Advanced Internet Computing Systems and Applications

  I agree with previous reviews that if you don't like writing then do not take this course. Every week you have to essentially write a 6-8 page essay and only in the final few weeks do you have to focus on the project where coding is optional to help you write the 15-20 pages.

  On the other hand, they are extremely lenient in marking. I peer-reviewed some extremely poorly written essays, but the averages were always 80+.

  The interesting part was peer reviews. Some seemed like they used ChatGBT to write it and few students gave meaningful reviews that help you improve.

  The TAs were also receptive to feedback. Students didn't like the brief one-sentence reviews from the TAs so the TAs started expanding which was nice to see.

  The exams are open book and no HonorLock is used, but they are on top of the weekly assignments.

  The topics were interesting, but the lectures had errors or were confusing at times. I feel like Data Warehousing and later concepts we didn't ingest properly due to the time constraints with the final project and final exam.

  Rating: 4 / 5Difficulty: 3 / 5Workload: 14 hours / week

• o8HMU4BGyQsYYgXng2bOOw==2024-02-05T15:45:26Zspring 2023

  Mobile and Ubiquitous Computing

  This is it. This is the worst class in the OMSCS. Yes, I have taken Software Architecture and Design. This is worse.

  Only 30% of your grade in this class is based on your own work. The rest is based on group projects. The volume of work in these group projects is impossible to complete alone or with 1 other person unless you're unemployed and not taking any other classes. So if you get stuck on a bad team, you are completely screwed. Your grade in this class is purely a matter of luck. You'd better pray that you have the good fortune to get at least 2 team members that actually do some work. Good luck with that.

  If your teammates don't do any work, you're completely screwed. The instructor will not allow you to change teams or request additional team members. You will not be given extra time, even if you can prove that your teammates are not responsive. I literally showed Ploetz screenshots of my team members explaining why they would not be contributing to the project. He did nothing.

  Let me state this again: YOUR ACADEMIC COMPETENCE AND WORK ETHIC WILL NOT SIGNIFICANTLY IMPACT YOUR GRADE IN THIS CLASS. The work that you actually do barely matters. Your team determines your grade.

  Rating: 1 / 5Difficulty: 4 / 5Workload: 30 hours / week

• Ki03VESWPL4A6uzTOc7CeQ==2024-02-01T11:37:24Zfall 2023

  Data and Visual Analytics

  worst class structure ever, lecture videos are totally useless

  Rating: 1 / 5Difficulty: 3 / 5Workload: 21 hours / week

• z9NQv9C9iU8cniecUaWM/Q==2024-01-31T14:52:09Zfall 2023

  Deep Learning

  Also posted on OMSHub,org

  I took the class in Fall of 2023 as my 6th class in OMSCS.

  Overall I really enjoyed the class and got an A but just barely.

  The class consisted of 4 assignments and 1 group project project, and 5 "quizzes".

  Prerequisites:

  Python proficiency, especially comfort with numpy python package since pytorch uses a similar syntax. Familiarity with machine learning. If you don't have this, I highly recommend taking the time to do Andrew Ng's machine learning or deep learning specialization on Coursera. Assignments I had to work on the assignments almost every day. They were very hard but if you were consistently working on it, checking EDstem, and office hours you could definitely get through them and learn a lot.

  Assignment 1 + 2: Deep learning basics and Convolutional Neural Networks from Scratch. I think the most useful thing I learned was how to do back propogation by hand and getting comfortable with using Tensors in pytorch.

  Assignment 3: Shortest assignment. Style transfer and visual explanation of deeep neural networks.,

  Assignment 4: NLP basics, RNN, LSTM, and Transformer Architecture. This IMO was the most interesting assignment. Language models like ChatGPT is built on transformer architecture so understanding them is very important.

  Quizes: IMO these were more like exams and the most stressful part of the class. You absolutely need to study for them. I did about average on these but I feel like quizes don't always reflect the assignments or the lectures very well.

  Projects: Your group has to do a deep learning project. My team did a kaggle competition where we looked at an image classification task for very large images (file sizes of >1GB). Kaggle is great because they provide free GPU resources (up to a certain amount per week). The class also gives you some GPU credits on Google Cloud but it is a very limited amount. We also had to submit a 6-page paper written in Latex document which is useful for those interested in publishing their results. The grading on this was very minimal. We had to submit the assignment within a few days of the end of the class so the TAs did not grade the report that harshly.

  Rating: 4 / 5Difficulty: 5 / 5Workload: 20 hours / week

• WrKfQ1jcSrBuYU1JSI1/pA==2024-01-24T03:34:55Zfall 2023

  Machine Learning

  I absolutely loved this class.

  The lectures: I had lots of fun watching them, the two teachers made me laugh. I would recommend to read the corresponding chapters beforehand because otherwise, you might lack some helpful knowledge.

  The papers: With chatGPT, writing papers in Latex was very bearable. chatGPT use is encouraged as long as you don't just copy from it and I had some great "discussions" that would in a remote study normally not be possible. I recommend to use a Latex template - in our semester, someone provided one and that saved tons of time. The feedback that is according to the professor super important came always too late to incorporate it so don't count on it.

  The exams: I read the book once, some chapters twice when I needed to lookup some algorithm, and I tried to understand the general topic of the papers. That was enough to get between 70 and 80 % on the exams. The questions I got wrong were often formulated in a little bit of a tricky way, so take your time to understand what they're asking.

  Ed Discussions: This was one of the classes where I least used EdDiscussions. That doesn't mean there were none, but since "stealing" code is so encouraged, and ML is such a hype, most of what I needed to know I found on the web.

  Office hours: I went to the first one and found them a bit wordy - after that I never went again,

  What I would do better: I would try to organize my code better. Much of what I did, I reused over the assignments and proper modularization of functions would have saved me quite some time.

  Rating: 5 / 5Difficulty: 4 / 5Workload: 23 hours / week

• /AO2b9uFNEUxLlYJ/EqCfQ==2024-01-21T19:40:55Zfall 2023

  Game Artificial Intelligence

  This is a good course, though pretty easy. The assignments are just really well designed and fun. I really appreciated the effort put into them as well as some of the optional activities like tournaments for some of the AI we designed. I didn't spend much time each week because I didn't have the time, but I absolutely could have sunk 40+ hours into some of the assignments and enjoyed it. Didn't have any C# experience going into this and I was fine, people who are newer (< 3 yrs) may have a bit of trouble, make sure to get intellisense running and save a ton of headache.

  The class gives you a good overview and implementations of some 'good 'ol fashioned AI' but has some opportunities to experiment with RL/DL as well. I really gained an appreciation of the difficulties of designing game AI and how different it is than what you see in ML papers.

  The downsides are that the lectures are a bit long/slow, and the class is not particularly challenging. Honestly though, I think this works as a fun class that has enough material that if you want to put in more work you absolutely can, and I'm a bit regretful that I didn't.

  Rating: 5 / 5Difficulty: 2 / 5Workload: 10 hours / week

• UxM4W8UQJZ4uBeXkBIQvBQ==2024-01-21T18:22:09Zfall 2023

  Graduate Introduction to Operating Systems

  The course material was very useful and practical. My current role is as an embedded software engineer and I have been able to put some of the principles to work right away.

  The workload for this course was much heavier than I would have expected. One good thing is that the projects are typically made available 4 weeks before their due date. Overall, I found the projects to be enjoyable, just too much work for one three-credit course.

  There is no textbook for this course, but several interesting white papers are provided as reading requirements. Most of these are more than 15 years old , but they are helpful at providing historical context with regards to operating systems and the concepts generally still apply.   Only the first half of the course actually focuses on operating systems. The second half of the course focuses on cloud and distributed computing.

  To be successful, you will need to be able to write code in C and C++. Many of the projects build upon work done in the previous projects or parts of projects. In order to avoid rewriting the same code over and over again, create abstractions and focus on implementing one piece at a time, rather than doing everything at once or using really long functions.

  Some of the projects do not directly relate to the course materials, which was frustrating. They are there to prepare students with practice and background ahead of the main portions of the project. However, everything is graded, so to succeed students need to write a lot of code.

  The TAs were generally responsive. Although sometimes they could be condescending when responding to students (many of which already have many years of work background in software development).

  There are two exams, each covering around half of the lectures. To prepare for the exams, students must review all of the lectures and white papers. The TAs do provide some crowd-sourced class notes from previous semesters, which are very helpful in preparing for exams. To prepare for exams, I would recommend taking notes as you go, reviewing all the class notes and reading the white papers highlighting any information that you think might help for the test. Some of the test questions were on obscure details from the white papers, such as recalling and comparing specific benchmark tests. Even with a lot of studying my test scores were in the low B range. Fortunately, the projects use gradescope and by starting earlier and refining their work, most students should be able to get 100% on the projects.

  Rating: 5 / 5Difficulty: 4 / 5Workload: 23 hours / week

• exenhSmf5lOOcSoZUrQd+Q==2024-01-20T20:13:36Zfall 2023

  Human-Computer Interaction

  This course is interesting for those who want to learn more about design process. I specifically selected this course because of Prof. Joyner. Lectures are engaging but there are a lot of assigned readings that are included in the midterm and final exams. However the exams are open book so if you're short on time and miss a few papers there is 2hrs for the exams, more than enough time to search for the answer. Also note that there are 10 weekly reports due. The reports are not difficult and as long as you follow the rubric, there should be no problems getting full marks. However, other students in the course complained about inconsistencies in grading - I personally only experienced this once where the same abstract I had used for several of my M assignments was docked marks but scored 100% with every other TA.

  There was also a group assignment in Fall 2023. If you are looking for a good group, try to join up with other students who are pro-active, many students started forming groups on Ed Discussions from day 1. There are four weekly check-ins and the final submission is a 30 page report and a substantial part of the grade.

  Rating: 5 / 5Difficulty: 2 / 5Workload: 10 hours / week

• exenhSmf5lOOcSoZUrQd+Q==2024-01-20T20:04:35Zfall 2023

  Machine Learning for Trading

  The content of the course is interesting and structured well. I believe this was the first semester they had the midterm and final exams as open book/internet - iirc the only restriction was no communication with other people during the exams. The questions seemed to be much more difficult than practice qns provided (which I believe reflects the exam style of previous semesters). Exams were not difficult but the wording of qns can be confusing and a 1hr time limit to review 100+ t/f statements means that you're generally working off of knowledge and only looking up a few qns here or there. Marks were scaled for exams - exam out of 110 and students would receive the mark out of 100. Essentially disregarding 10 incorrect answers.

  As others have mentioned the assignments are extremely detail oriented. There is a long document with all the assignment requirements + a 1.5-2hr weekly TA session going over quiz answers and walking through the assignment. I got full marks on all the coding sections but lost marks on seemingly unimportant details in the report like a line color on a graph. It would be great if the auto-grader could provide more informative feedback as all the assignments are related. Receiving marks and feedback 4 weeks after submission sometimes delays progress on subsequent assignments. Start early on assignment 3 and 8 if you can, those are the most time consuming! Most other assignments required <5hrs to complete.

  I would recommend this as as starter course for OMSCS. Prof. Joyner is top notch, the content is interesting and not difficult. TAs are very responsive, make sure to keep an eye on Ed Discussions. It's a great course to ease back into studies.

  Rating: 4 / 5Difficulty: 3 / 5Workload: 15 hours / week

• y3EeS2GfivMVqxWb4JBVcA==2024-01-20T18:11:27Zfall 2023

  Human-Computer Interaction

  This course is a really good introduction to understanding the science behind how we interact with computers. However, it can be somewhat tedious. Most of the course work is writing papers. They aren't heavily scrutinized for grammatical perfection, but the content that you include does need to be correct. The provided structure for the paper in Overleaf was more than enough for me to never lose points on structure. The group project is just a rehash of your own individual work prior to it. It isn't very useful in my opinion, but adds a lot of anxiety due to group members and their behavior/effort. I ended up with an excellent group, but could've written and done the work for the entire project in a couple of weeks instead of it being spread out over 5 weeks of meetings and working with the members of the group's different schedules and input.

  Overall, the class wasn't too hard, but the pace is sometimes rough (some weeks have multiple large projects due). It was an interesting class and I say that as someone who has never done much in the realm of design or "frontend" work.

  Rating: 4 / 5Difficulty: 3 / 5Workload: 15 hours / week

• 3XCJT8ZLLwaBH3r1BaCReQ==2024-01-19T20:09:45Zfall 2023

  Introduction to Graduate Algorithms

  I was one of the students who had to retake that course because I did not do well on it the first time I took it during the Summer semester. I got 69 and ended up with a C in the class the first time. I think I made the mistake of taking that class during the summer with another class and since both classes had weekly homework assignments, it was really hard for me to manage both classes. I retook the course in the Fall as was average above 90 on the class. Unfortunately I did not do well in the last exam, but I scored enough where I did not have to take it a third time. This feel the pain of other students who had to take that class more than 2 times and honestly I can understand the frustration. When I was taking that course, one of the students wrote a long entry in our Canvas expressing their disappointment on the difficulty of the class and how it's preventing them from graduating. That student really scored bad in one of the exam and had already taken the class 2 times. I wanted to share a video where I explain the strategy I have used to pass the course https://youtu.be/JbjUhfrRcmA . Hopefully this can help other students who had to retake the course and also could be helpful for students taking the course for the first time.

  Rating: 5 / 5Difficulty: 5 / 5Workload: 20 hours / week

• XqI8jd8i1eJfCnKj2v8g+w==2024-01-13T00:13:21Zfall 2023

  Advanced Topics in Software Analysis and Testing

  The course really does a good job of illustrating that static and dynamic analysis can do a lot to ensure your software is error-proof. However, a lot of the methods are not super relevant in industry, which isn't necessarily a downside of everyone. The labs were pretty well organized but the lectures were hard to follow at times because the scope would change quickly.

  Rating: 3 / 5Difficulty: 2 / 5Workload: 8 hours / week

• oI/ZNriAtlOWVqKKTb3FUw==2024-01-10T21:20:16Zfall 2023

  Data and Visual Analytics

  Context for me, I had 0 background in programs outside Python/R/SQL, I double majored in economics/stats for undergrad and work in banking as a DS. This course is definitely a tough one to grade. On the one hand you will in fact come out learning a lot but all of it will be self taught. The lectures aren't useful at all. You have 4 homeworks and each one gets three weeks to be finished. This is definitely a fair amount of time, 10 hrs a week will get you to finish them by the time it's due. I HIGHLY recommend you start early on these HWs, as in the weekend they come out do at least 2 questions (the one worth the most and an easy one). Then the next weekend another 2 (the next biggest question and next easiest), and then finish with the final question (i think there was always only 5 questions.) You will see Python classes/functions here, you will see a lot of SQL (lite) here too and some are very tricky queries, you will see Pyspark, Tableau, and intros to amazon AWS and docker (which are musts in the data science world). But I'd say the main component of the course (maybe 40%) is D3, and that is awful, those questions will make you want to cry, 2 pages of code for a bar plot. 10 pages for an interactive visualization. That's undoubtedly the toughest part of the homeworks. I would recommend you pick you groups ASAP and before even thinking about the project agree you'll use it to help each other out in the hw (NOT AS IN PLAGARISM) but as in "hey for some reason this query works locally but in gradescope i get an error, what could it be?" or "any good resources for this D3 part" or "How can I make my graphs dots change color". Specific things like that, because a lot of the time SMALL parts of your code will be what's not letting you get full credit. Getting a good team is a must, if you feel like the group isn't helpful FIND ANOTHER GROUP. The group project is worth 50% of your grade and is the easiest part of it. Do not screw it up. A good group and good project will guarantee you at least get an 80 even if you struggle in the hws (60s/70s). I ended up getting an A in this course (actually 89.90) but it was rounded up and I bombed (40% F) one of the homework assignments (tried to do the docker/aws HW3 in one weekend, DON'T do this.) Thanks to my groups predisposition to help each other out, we did really well on the other HWs and got an A in our final report. I'd recommend taking this course AFTER Computing for Data Analytics and also Computational Data Analysis. I took all three fall 2023 and the dataviz+CDA combo whilst working full time nearly killed me, I was very stressed out. Don't do this, but you can deff pair this with an easier course (MGT or other mandatory intro courses) whilst working (probably even two if you really want to speed rush).

  Rating: 3 / 5Difficulty: 4 / 5Workload: 10 hours / week

• oI/ZNriAtlOWVqKKTb3FUw==2024-01-10T20:55:23Zfall 2023

  Computing for Data Analysis: Methods and Tools

  Very fun course and a great introduction the the master's degree. The basic layout is you get a jupyter notebook with very great and detailed introductions in %MD for each topic, then you get some empty function which you run until you pass the functions test (already built). Midterms and final follow this exact format, all tests are open book so if you've done the HW saving a few stackoverflow links or documentation will go a long way to help you finish quicker. The exams are very fair, if you know already know python and did all the HWs I'd even say easy. However if you don't already know python it will be trickier. This course is perfect to prepare you for pair coding interviews or any coding process you might be involved in. All exams have more points awarded than are required for a 100%, so you can do something like earn 10 points out 16 possible and get an A! Overall I loved this course, if your new to programming you will learn A TON! If you already knew, it's an amazing refresher because honestly who much about dictionaries? This should be a prereq before moving onto other courses but this code will set you up well for ISYE 6740 (CDA) (highly recommend), and then both of these will set you up as best I can imagine (nothing will prepare you for D3) for CSE 6242 (dataviz). I stupidly took all three together but you can see how a good structure would've been.

  Rating: 5 / 5Difficulty: 2 / 5Workload: 6 hours / week

• oI/ZNriAtlOWVqKKTb3FUw==2024-01-10T20:45:09Zfall 2023

  Computational Data Analysis: Learning, Mining, and Computation

  This was a great course. A very strong range of algorithms which you get to code for each one (sometimes from scratch) with strong math proofs and demonstrations behind each topic. Don't stress about the math it's nothing too crazy just abstract derivatives (chains rules, log properties), Lagrangian multipliers (but the professor always goes over the math very well for each algorithm). While the course certainly covered these topics in depth, it wasn't the focus (about 20% of the points in HWs). The lectures were very well balanced in theoretical, math and practical terms. Unlike other courses where you never have to see the lecture videos, here it's an absolute must, it will help you immensely in doing the homework assignments (6 total), you also definitely should use the starter code skeletons for each topic and homework assignment. You learn about a bunch of machine learning and stats algorithms, from an overview of what they are, do and used for, to the their objective function, what they optimize and finally how to use it in Python or matlab. Course deals a lot with images, so a lot of the time you aren't working with traditional dataframes rather their favorite is a series of Yale face pictures reduced in dimensions for modelling. I had never worked with images so learning how to get rows and columns from a picture was tricky but it's not too bad if you have worked with Python before. This was my third course i took it simultaneously with DataViz and Computing for Data Analytics. Definitely a mistake you should deff take CfDA before this class and NEVER partner it up with DataViz, I got an 88 in this class which is a flat B. But I can genuinely say I learned a ton and it was worth the suffering.

  Rating: 5 / 5Difficulty: 3 / 5Workload: 10 hours / week

• gdeUwiiQMfe5MQuebmSzOg==2024-01-10T09:06:19Zfall 2023

  Human-Computer Interaction

  HCI is a well put-together course. The course is being revamped after Fall 2023 so I cannot speak to the current iteration of the course. For Fall 2023 the amount of writing was relentless, but manageable. There were weekly 6-8 page papers due for 10 consecutive weeks, an individual project, a team project, and two exams. The course material prepared one appropriately for all the assignments and exams. Dr. Joyner is a teacher and communicator at heart, which makes his course a pleasure to take. I was able to use concepts learned in the class in my day-to-day work immediately.

  Rating: 5 / 5Difficulty: 3 / 5Workload: 9 hours / week

• C17orAyBtVbxMT3SftseFw==2024-01-10T07:30:18Zfall 2023

  Computer Networks

  I just took this class in Fall 2023, and received an A. This is one of the most straightforward classes in OMSCS. Take good notes. Do all of the quizzes. Spend time on the programming assignments, and make sure you really read what the TAs are asking for. Ask for help from your fellow students in either EdStem or the Discord channel (unofficial, but usually a student starts one every term).

  This class used to be completely different content altogether (used to be Udemy based) when I started with OMSCS, which is why I initially skipped it. However, Professor Konte did a great job of revamping the lecture content, and making it more modern to what OMSCS standards are like today. I only wish I had taken this course earlier in my OMSCS schedule.

  Rating: 5 / 5Difficulty: 3 / 5Workload: 12 hours / week

• Phh7FsZIJClc3m8RyfXSqw==2024-01-10T04:15:02Zfall 2023

  Introduction to Information Security

  1st class taken with a 93% (I had 100s on the first 5 assignments, so the last 2 I basically did the bare minimum to secure an A)

  This course is... interesting.

  "You get out what you put in" is somewhat accurate. Besides 1 or 2 flags, the only way for you to get a flag is to actually learn something. Problem is, learning something can only really be done by self-study. There isn't any lectures or instruction besides links to Research Paper's (which no-one is reading let's be real).

  Office hours are mostly there to poke fun at you, as the TA's are riddle master's narrowly avoiding answering any of your questions.

  The ONLY way to learn anything directly from this class is those ED Discussions. Those guys are troopers pointing you to articles that will teach you what you need, but it's still quite difficult to realize what it is you're looking for.

  Can't say my SWE career will really use any of this, but it was interesting. Not sure I'm the best person to write one of these as I'm grade focused not learning focused, but that being said I still managed to learn a decent bit from this (Wish I could have learned more about the TCP/IP stuff, but alas)

  To be clear, this class isn't hard. I'm sure there are far more time consuming and difficult concept classes. My gripe with this class is if there was maybe a 30 minute video explaining things, the workload would really only drop to about 3 hours a week. Majority of time spent is basically research rather than studying the material.

  If you are good at Ctrl+f and really know how to google specific things, I'd say about 7-8 hours a week, assuming you just want to finish the assignments.

  Rating: 3 / 5Difficulty: 3 / 5Workload: 7 hours / week

• +jTAa/Y57/V1BqtRCslpQA==2024-01-09T06:09:03Zfall 2023

  Special Topics: Business Fundamentals for Analytics

  Very basic course. The lectures and video conferences cover all the material. I took it along with CDA and was strapped for time so ended up doing last minute preparations for most of the exams. I think the topics were interesting and very applicable if you're inclined to any kind of bussiness. But the structure of the exams was quite annoying and had to memorize a lot of theoretical knowledge.

  Rating: 3 / 5Difficulty: 4 / 5Workload: 4 hours / week

• +jTAa/Y57/V1BqtRCslpQA==2024-01-09T06:03:38Zfall 2023

  Computational Data Analysis: Learning, Mining, and Computation

  Great course! I think it's a must! Great intro to various ML applications. I combined it with MGT 8803 which made the time commiment restricted. I wish i had more bandwidth to provide towards this course. It was a tough course for me because of lack of bandwidth but overall a great course. Make sure to find a good teammate for the project.

  Rating: 5 / 5Difficulty: 5 / 5Workload: 14 hours / week

• vsibVbdFfYHQ84sN6cGhvw==2024-01-09T03:53:55Zfall 2023

  Mobile and Ubiquitous Computing

  Overall, I would not recommend this class. If you are in the HCI specialization, then prepare for a below average experience.

  The course content is comprised of 4 professors teaching the subject. The course material is relatively straight-forward and the content/reading checks are incredibly easy. There are unlimited attempts so retaking them in encouraged. This will amount to your overall participation grade. No issues here.

  The communication of the TA's/mentors running this course is where the course became pretty frustrating. A bit more on that in a moment.

  The other issue is that there is a project which is worth more than 50% of your overall grade. I believe the group you are assigned will be the deciding factor in your attitude towards the class. You are able to choose the members of your group so find a good group early on. This is my most valuable advice to you. If you are put into a poor group, you will end up doing all the work unsure of your progress at all times due to inconsistent feedback from the project mentors.

  It seems that Professor Starner does not give his TA's a full understanding of the project requirements (either that or there is some miscommunication) as we were at odds with our assigned TA multiple times making the project absolutely unbearable at times. Literal weeks were wasted going back and forth trying to get an understanding of our goals and what we could and could not do.

  The final exam is easy if you study as the content is not difficult. There was one assignment which required an Arduino board. That was actually very fun and my favorite part of the course. In that moment, it felt like a ubiquitous computing course learning about sensors and making actual circuits. Only caveat is that it would be great if you didn't have to buy a $45 dollar kit only to use it once and never touch it again. If I could go back in time, I would center my final project around the Arduino somehow.

  Some TA's were great. Answering as much as they can and trying to organize as best as they could with what they were given.

  It's not that I did not learn anything, it's that my memory of this class is tarnished due to what could have been avoidable if more care was taken into organizing the class.

  Rating: 1 / 5Difficulty: 3 / 5Workload: 20 hours / week

• ScojLJabnNDE3kGW4WxudA==2024-01-07T10:22:54Zfall 2023

  Game Artificial Intelligence

  Class is pretty hard, but manageable. Some of the homework was difficult to me for a person without too much AI experience, Lectures do follow homework assignments, but still a struggle. I had to go to TA office hours in order to get past some blockers, and some homework assignments clicked much easier than others. The good thing is that the second half of the semester is super easy, this class is very frontloaded in the workload and difficulty. By the time you finish the mid-term, you have done all of the hard assignments, and the mid-term is significantly harder than the final. I got around a 50 on the mid-term and an 80 on the final.

  Rating: 4 / 5Difficulty: 4 / 5Workload: 15 hours / week

• fpnO7tAo5zfo/9+lngZ9pA==2024-01-07T01:34:15Zfall 2023

  Artificial Intelligence Techniques for Robotics

  This was my first course in OMSCS, and I felt it was a very good introduction. I got an A in this course, and enjoyed the course, with content including Kalman filters, kinematic bicycle motion (essentially some trig), histogram filters, particle filters, some search algorithms (policy, A*, etc.), SLAM. Would definitely recommend this class, especially as a first class. Exams are closed book, closed notes, straightforward, and not too difficult; I only spent a moderate amount of time to study for the exams.

  Some points that I noticed:

  • Lectures were engaging and informative, focusing on concepts and code application.
  • Course is not overwhelmingly challenging.
  • Projects were fun and a great application of lecture material, while not being overly tedious or difficult. Some of the code can also be referenced from the lecture material, which provides a good starting point.
  • Excellent TA's, the tutorial lectures by TA Chris were very helpful to get an intuitive understanding of the material and tips for application going beyond lecture material.
  • Lots of tweaking of certain parameters to achieve high marks on assignments.
  • Potentially slightly outdated content, covering more traditional AI robotic topics.
  • Lectures alone will probably not be as helpful as the problem set lectures and TA Chris's tutorial lectures.

  Rating: 5 / 5Difficulty: 3 / 5Workload: 14 hours / week

• B7WyA0LDsvtWNQG6EZea/Q==2024-01-06T14:51:19Zfall 2023

  Machine Learning for Trading

  Summary: Overall, this course was just fine. If you're like me and just want an introduction to ML and have no ML background, then this is a great course. However, it is taught like an undergraduate course, so there is less personal responsibility, but the "coddling" takes up some unnecessary time.

  Pros: -Great intro to ML -Easy, material isn't difficult to understand -Gives some good introductory material to ML and Finance

  Cons: -Project and exam expectations are distributed across Ed discussion, weekly recorded office hours, and the course website. You need to read all of these very thoroughly to understand the expectations of the projects. -The reports are sometimes tedious. These aren't too bad but they take a lot of time and don't add much more value than a question/answer worksheet would.

  Rating: 4 / 5Difficulty: 3 / 5Workload: 16 hours / week

• uQnc1fPbJtUhBS/I6DF3IA==2024-01-06T04:01:13Zfall 2023

  High Performance Computing

  This was my 7th class in the OMSCS. It's got one of the best course material I've done, and I feel like I'm a much better engineer for it. The labs are hard but interesting and I definitely feel like I grok the concepts well afterwards.

  Unfortunately the teaching staff were the worst I've had. I found them generally disdainful of the students, and my understanding of HPC was not aided by them. As an example, a student asked how a particular approach could possibly be efficient, given the overhead it requires. The response was "It's magic :-)", and no further elaboration.

  As mentioned, the labs were difficult - I spent the entirety of several weekends on each one, and some performance tuning in the week when I could fit it in around my job. That being said, I was able to get good marks on them so it felt like the hard work paid off. There was also an extra credit project (which used to be a normal lab). The performance of your code is a significant part of the marks, which isn't surprising of a class called "High Performance Computing".

  The midterm and exam were both very difficult. The midterm had a median of 46% and the final had a median of 45%. Both got flat grade adjustments. I didn't feel like they were good assessments of my understanding of the course material, and the instructors admitted that some questions were ambiguously expressed (which I read as "poorly written").

  The course material has been shuffled around a bit since it was recorded, so I found there was some jumping around required to have the required background for the labs.

  Overall, I'm glad I took this course as I feel like a better engineer now than at the start of the semester.

  Rating: 4 / 5Difficulty: 5 / 5Workload: 16 hours / week

• OurjdTA81rEMUz00lB7TeA==2024-01-06T01:23:06Zfall 2023

  Special Topics: Quantum Computing

  Lectures are all purely theoretical and assignments are all purely application based. Exams ask questions never covered in any lecture or assignment, particularly stepping through examples and doing math of certain algorithms never covered in details. I wish the lectures covered more practical applications (like what the assignments are) and then have the assignments be more difficult versions but having the simple examples to work off of. They never show how to code in qiskit or how it connects to theory, but I wish they did. Other than that qualm, the course teaches a vast swath of new information so if you work and don’t have time to watch youtube videos and read the textbook, retaining information is difficult. I would analogize this course to an intro to digital logic course in undergrad in terms of newness of material and the fundamentals of logic and algorithms it teaches. The theory taught on a high level is good. Error messages in grad scope for coding assignments are awful. TAs are pretty rough with grading reports. Definitely recommend this course for the content if you’re at all interested in quantum computing and the potential power it holds.

  Rating: 4 / 5Difficulty: 4 / 5Workload: 10 hours / week