100 Most Recent Reviews
dLcW+Gd8NYobOQsgrbFgZg==2024-10-10T22:15:10Zsummer 2024
Introduction to Graduate AlgorithmsThis was probably the hardest class I took in OMSCS (this was my ninth class). What another reviewer wrote about Jamie (he only posts memes taunting students asking for help) is sort of true. There were a couple of other things that made me think the TAs were not up to teaching this class. This was definitely the most stress I went through in any OMSCS class (needed a B to graduate).
Don't listen to the people claiming that tons of people have to retake it. It's true that some people retake it, but your odds of getting an A or a B on your first try are about the same as any other class (I made a Monte Carlo simulation based on that publicly available Lite data demonstrating this). Also, it's true that many students just do not grasp the material, so they complain too much unjustifiably. You can tell many people have no idea what is going on by reading the regrade threads.
The grading on homeworks was far harder than the grading for the tests, IME, but that may have only seemed to be the case because I learned from my mistakes on the homeworks and applied them to the tests. Anyway I did much better on tests than on homeworks.
This was my ninth class and until my grades stabilized and I knew I was going to get an A or a B I wondered whether I would continue with the program and try again or just quit. I'm 37 years old and I don't really need this degree. It was kind of depressing. Luckily I got the B.
Rating: 5 / 5Difficulty: 4 / 5Workload: 20 hours / week
K/Vo7XqJ36yhITh8Gq24XQ==2024-10-06T03:14:35Zsummer 2024
Computational PhotographyI am taking the course right now (7th week fall 2024). I selected the summer 2024 due to the limitation on this site. The quality is very low. The lectures have low value when you compare the time instructor put into them and off course you put in to get/learn something out of them. Too much guideline to let themself free of guilt. This course technically ruined the image I had of Gatech. Their care is using OpenAI, They don't understand that people use OpenAI when they do not understand the course and face a task that is totally new to them. There is no challenge/learning when you feel helpless in an assignment. I wish I could get refund.
Rating: 1 / 5Difficulty: 5 / 5Workload: 20 hours / week
0w+Llg76gamuEtxFQAS/Gg==2024-10-05T18:38:30Zsummer 2024
Introduction to Graduate AlgorithmsI come from a mechanical engineering background so this was my first algorithms course. I'm of average intellect at best and finished with over 96%.
Struggle through the homeworks trying to figure them out yourself rather than looking online and you'll be fine, both in the sense of learning and avoiding cheating.
I suspect the whining about this course is from those who never learned to solve problems from first principles and instead use Google/llms at the first feeling of adversity.
Rating: 5 / 5Difficulty: 3 / 5Workload: 15 hours / week
z6RtzfxHcCnFa5KMd920Ag==2024-10-01T18:40:12Zfall 2023
Introduction to Graduate AlgorithmsThis is one of the worst courses I have taken. Grading is not clear, Instructions are just scattered everywhere through Ed. TAs are unhelpful and not skilled enough to teach such an important for graduation course. Also there's basically no option but to pass it in most of the specializations and by passing I mean more than a B since it's a core subject. I am repeating it for the third time just because it's the only blocker between me and graduation. MOST AWEFUL EXPERIENCE!
Rating: 1 / 5Difficulty: 5 / 5Workload: 20 hours / week
fqEC+EKjdsF3KG0KXcy/2Q==2024-09-29T18:40:36Zspring 2024
Introduction to Graduate AlgorithmsIf you're considering Georgia Tech's OMSCS program, specifically CS 6515 (Graduate Algorithms), take a moment to rethink. Despite the material not being inherently difficult, the course's structure, grading, and teaching staff create a frustrating and toxic learning environment.
The teaching assistants (TAs) are, to put it bluntly, unhelpful. Many of them seem to lack the competency or willingness to guide students effectively. Communication is poor, feedback is delayed, and the overall support system feels like it’s set up to watch students struggle rather than succeed. Jamie R. McPeek, in particular, has been a negative presence. This individual should not be allowed on any teaching staff due to their contributions to the toxic culture of the course. Instead of fostering a collaborative and supportive space, they and the rest of the team seem to exacerbate stress and confusion.
One of the most infuriating aspects is the lack of an autograder for immediate feedback, especially considering that nearly every other course in the program offers this. You’re left in the dark after submitting assignments, forced to wait until grades come back – which often feels arbitrary and inconsistent. It's astonishing that a program that prides itself on tech innovation can't implement something so basic for such a crucial course.
Another glaring issue: the course is structured so that most students take it as one of their final courses. By this point, you’ve likely invested a significant amount of time and money into the program, which leaves you feeling trapped. It plays into the sunk cost fallacy, making students feel like they have to see it through despite the frustrations. Many end up taking the course multiple times – a setup that feels like a cash grab rather than a genuine educational experience.
For prospective students, if you're eyeing OMSCS, I'd strongly recommend looking into the Interactive Intelligence specialization. It allows you to bypass this course entirely, preserving both your sanity and your wallet.
In summary, CS 6515 is a masterclass in poor management and toxic educational environments. I cannot, in good conscience, recommend this course or any class associated with these professors and TAs. Proceed with caution.
Rating: 1 / 5Difficulty: 5 / 5Workload: 15 hours / week
uEA3ipq+dRm4hM/FmB2J8w==2024-09-26T08:03:49Zfall 2023
Machine Learning for TradingLearn 1+1=2, homework is 1+1=?, and when you put 2 into the answer, it gives you zero for grading, since the correct answer is 2,000E-3
Rating: 1 / 5Difficulty: 1 / 5Workload: 40 hours / week
OCFIzxthJxP6v2FNM4k2vQ==2024-09-26T04:52:47Zsummer 2024
Introduction to Graduate AlgorithmsThis course adds too much unnecessary pressure to students – for example, the staff told us that the grade has been released, but we may still be charged some penalty. When will we know? Within the next a few days. The cutting throat feeling just does not make sense, especially during the exam period. Not to mention the course workload is challenging itself.
The Ed Discussion is like a battlefield every day and I can't avoid reading these intense messages because of the coursework, which has really a negative impact on my mental health. I really want to help my peer students or seek help on my own regarding the course topics, but my posts are just that easy to be deleted, judged as plagiarism or receive no responses. I feel helpless. My family starts to worry about me and suggests me to pause, so be it.
If you must take this course to graduate, I strongly advise you to hold off until the last one, as it will affect your confidence for the remainder of the program – like what I am experiencing now, despite I have an A in my current grade and done all the coursework as suggested – I still feel that I have been subjected to a lot of unnecessary stress that has caused me problems in my life. Not worth it. Please, please improve this course. We are here to learn and not to be bullied or tricked. Some memes are just not funny but rude.
Rating: 1 / 5Difficulty: 5 / 5Workload: 30 hours / week
rh2jpC5aK05qOGhRKUqa3A==2024-09-24T16:54:33Zsummer 2024
Introduction to Graduate AlgorithmsThis is one of the best examples of a course that's intentionally made more difficult than its supposed to be through grading and expectations sent through 10,000 chicken scratch ed discussion posts. You have to read all 10,000 after work and keep track of everything. Everything, from the condescending TAs to the unrealistic expectations makes this one of the poorly designed mandatory courses.
Using the world class Graduate Algorithms grading to rate this scores as below:
PROFESSIONALISM: 5/20 (5 being for the few that actually help) COURSE MATERIAL: 2/20 (2 for linking ed discussion threads, everything else is an incoherent chicken stratch for notes) GRADING: 10/20 (-10 for expecting answers matching model solutions through a discussion amongst TAs rather than defined answers from a verified source) OVERALL: 5/20 (Rounded down to match the gold standard rating) PASS/FAIL: FAIL
We recommend retrying with 1 million attempts at syllabus changes but the grading standards will not change. For a student they can expect similar scores as above in exams and HWs.
Rating: 1 / 5Difficulty: 5 / 5Workload: 30 hours / week
DRhNRbP80f8K8JAh8aMKfQ==2024-09-21T20:23:22Zsummer 2024
Software Development Processnot bad. class quality and TAs are good.
problem is group project. since this is one of core requirement class. there's lot of people in this class.
It means some ppl are type of "bus passengers" and literally do nothing for project. Too many people = there's always toxicity.
Rating: 2 / 5Difficulty: 3 / 5Workload: 12 hours / week
53/LnunEUaJSlio8aWpS+Q==2024-09-21T05:20:59Zsummer 2024
Special Topics: Quantum ComputingI really liked this class. The front-half of the course was how quantum computing and algorithms work in theory, and the second half focused on current research implementations. The math is heavier earlier on but not overwhelming, especially if you come from an engineering or math background. Familiarize yourself with bra-ket notation and matrix operations/general linear algebra beforehand. Otherwise, I think you can pick up on the math you need during the course. Others have noted that the instructions aren't always clear in assignments. This lack of feedback on GS and the instructions can be very frustrating, but I would say the majority of the assignments are clear enough or have been addressed on ed discussion pages. Overall I think this class is worthwhile if you're at all interested by quantum theory or enjoy learning about some niche corner of computing systems.
Rating: 4 / 5Difficulty: 3 / 5Workload: 12 hours / week
qe9J9yTwZwnGmriFvM46Bg==2024-09-17T19:43:30Zsummer 2024
Introduction to Graduate AlgorithmsI feel like the TAs try to trick students and bully those who are in the CS track, there is the hundred way incentive student learn Algorithm, but they choose that toughest approach. You have to guess the purpose of the homework or quiz because it's not about helping students understand algorithms.
Rating: 2 / 5Difficulty: 5 / 5Workload: 25 hours / week
dkGbJ/23fOo1dX+pe6J8Qg==2024-09-15T17:48:23Zspring 2024
Introduction to Graduate AlgorithmsClass isn't difficult, but the way it is taught and the TA's who are head of instruction are honestly what make this class painful. However, i do understand given the size of this course there is no other way to handle it, but I do wish the TA's were a bit more professional. From my experiences in undergrad, I've honestly never had such a poor experience before, but I guess you get what you pay for out of this program
Rating: 1 / 5Difficulty: 3 / 5Workload: 12 hours / week
FI2nrW7+VsXdUQgLu1QNEg==2024-09-10T00:54:25Zsummer 2024
Special Topics: Financial ModelingNot as easy in my opinion as some make it out to be; Took me more than 5 hour average; To be fair, Dr. Garner really tries very hard during office hours to explain content, course just went over my head
Rating: 4 / 5Difficulty: 5 / 5Workload: 12 hours / week
Slt0liqjgkPgkMV3ytHVZA==2024-09-08T17:17:42Zsummer 2024
SimulationI took this class this summer and if I were to go back and take it again, I'd say make sure you do the practice exams with you cheat sheet and under time constraints. If you struggle with a practice exam question, make a note of it, and mark it down on your cheat sheet. Every so often I see myself revising the cheat sheet all again and again due to the feedback I got when doing the practice finals. If you think that 2 practice exams isn't enough, I'd go and look up textbooks that cover the same math and stats content as what is being taught in Sim. (Make sure it covers RVs, Prob Dist, etc) and do those questions. Honestly Sim is just a math course with applications using ARENA. Also,
I'd recommend getting a good scientific calculator and leverage their stat's functions there. I personally bought the FX 991 ES and it helped so much because it had integrals and you can switch modes where you can calculate binomial, normal, and Poisson (just look at the calculator guide and see how to do it). The project is lenient as long as you follow the rubric. We picked a topic where it felt like a research paper than anything (we did literature review, methods, results, discussion . Any additional requirements we list it in the annexes). But overall I'd say your main focus for this course is the practice exams and whatever question you can get ahold of using other probability/stats textbooks.
Good think about Dave is that he knows how to teach without reading the script so its more engaging then, lets say ISYE 6501 where Dr Sokol reads off the slides a lot (no hard feelings to Sokol, but I gotta give aura points to Dave for not reading off the slides). I never really had to look for external resources beyond Module 2 and everything is pretty much self contained as he said.
Its a great class overall, it prepares you for more advanced classes like CDA. Definitely a well structured and well run course!
Rating: 5 / 5Difficulty: 4 / 5Workload: 20 hours / week
aP7oY6oWsbmlUHD7BYxWuw==2024-09-03T04:07:00Zspring 2024
Artificial Intelligence Techniques for RoboticsI'm a SWE in big tech for 5 years now and I do have a background in engineering as well. I got a comfortable A.
I waited till I had taken other courses before rating this course since it was the first one I took in the program. Now being on my 4th course, I think I can comfortably rate this course as my best course so far, by far.
The course is very well organized, from Ed discussions to Canvas. The material is fun and engaging, the projects are awesome, challenging and unique. The TAs are probably the best in the program you will ever come across. There's one particular TA, I don't want to name drop, but you'll know him when you see him if he's still there. Honest opinion, he is too good for that job. God bless him. He singlehandedly made the course so much fun with his tutorials. The lead Prof. is pretty good as well at answering questions during OH. The key to this course is just to start early and try to finish the projects a week before the deadline. That way, if any project seems a bit too difficult, you have that extra time to work on it.
I highly recommend.
Rating: 5 / 5Difficulty: 4 / 5Workload: 12 hours / week
0YbDOzq/1p9Cvnk219PcBw==2024-09-01T06:41:28Zsummer 2024
Machine Learning for TradingWhen to take: I took this course in the Summer and I believe that is the mistake I made. Every weekend you have a project which makes it very difficult with work, so will highly recommend taking this course during either Spring or Fall.
Assignment and Lectures: The course content is easier and a very good introduction to someone who has no/less financial knowledge so worked great for me. The assignments are straightforward but do take time to understand or sometimes make it work in the environment so I will highly recommend putting enough time there.
READ THE REQUIREMENTS PROPERLY! I made the stupid mistake of not reading the requirement properly for one assignment and lost 20 points. Since the assignments are easy, these requirements matter a lot.
Most time consuming assignments are assignment 3 and 8 so start early for these.
Quizzes: The questions are confusing which I think is on purpose to ensure that you understood the concept and might make you lose points, since this course has a pre-defined grade structure, I will recommend putting time for lectures properly.
Staff: Super helpful staff and TAs, great response time and provides great resources. In case of any doubt, post a question and they will respond lighting fast. Not exaggerating, they have done that.
Overall I will recommend this to anyone who has less financial knowledge and would try to experiment with this domain.
Rating: 3 / 5Difficulty: 4 / 5Workload: 8 hours / week
25ez8RmfKZWX6J6+MSJ+fw==2024-08-27T02:49:31Zspring 2024
Database Systems Concepts and Design- Background: I have 8 years of experience as SDE. I got a B, with exam and project scores ranging from 80-90.
I took this course in my first semester along with Advanced Software Testing & Analysis. If I had done more research, I probably wouldn’t have chosen it.
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The content was pretty basic—more like an undergrad course on ER design and server-client app development. I was expecting to learn about database algorithms and more advanced topics.
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The exams were manageable, but the team project required a lot of effort to meet all the detailed requirements for an A. Make sure you’re in a team with both front-end and back-end skills.
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Key Takeaways: If you have a CS background or SDE experience, skip this course. If you’re from a non-CS background and want to learn SDE, databases are important, but this course isn’t the best way to do it.
The most ironic part of this database course? I ended up getting hands-on experience with React. LOL
Rating: 2 / 5Difficulty: 3 / 5Workload: 12 hours / week
zGuMkfD+0PJ9UKg0xt9o+g==2024-08-27T00:12:47Zspring 2024
Introduction to Information SecurityI enjoyed this class, but some days I wanted to throw my laptop out a window. The class has an ever-changing set of individual projects. If I recall correctly, each one was of a capture-the-flag variety. Most of the projects help you understand how a programming language can be used to exploit a system. The Log4Shell one was eye-opening.
The class can be incredibly frustrating and incredibly rewarding during every project. Most of the time, the students have very little idea how to start. Then, little by little, people start getting it. Then, those who figure out the tricks start leaving hints for everyone else. As the project period moves on, the hints get more and more overt until someone eventually overdoes it. A hand gets slapped, and the hints keep coming.
If you happen to know one of the tools well, your biggest challenge will be overthinking your problems. Even if you don't know the tool well, you'll probably overthink things. Read instructions very carefully. Read them several times. Help others when you can - they may lend you a helping hand later on.
Rating: 4 / 5Difficulty: 3 / 5Workload: 20 hours / week
9YsRFGtC0IgtidFrG1xvDQ==2024-08-24T19:38:22Zspring 2024
Artificial IntelligenceThis was my first course in the program. I did ML in the Summer. Both courses I got an A. My background was an undergrad in Stats and working in the field of Data Analyticsfor 3 years.
Overall, with my background, this was a great first course to get back into school mode. The assignments are auto-graded, so you get immediate feedback, and the exams are very difficult, but week-long so they allow for some leeway in the schedule. A background in Stats paired really well with a lot of the material, such that I could focus on improving my programming/implementation skills. This probably contributes to my reported workload being lower than the average.
Assignments: I found the first assignment extremely challenging, but then something clicked and the others went more smoothly. It is the only assignment I didn't get a perfect score on. Coming from a more theoretical background, it was really neat to implement the different algorithms from scratch, and even led to a few clarity moments on stuff like E-M algorithms, Naive Bayes Classifiers, HMMs which I thought of as black boxes before. A2 was my favorite though, building AIs to perform games is just... fun!
I think I should mention, you cannot leverage anything prebuild except approved libraries - the goal is to build these algorithms from scratch and really understand how to implement them. That said, the course focuses less on using the algorithms to solve problems, and more on implementing them, so its usefulness might vary depending on what you're looking for.
I strongly suggest reading the book, pretty much a requisite to do well imo. The supplemental material was pretty interesting as well, and a lot of the times helped, but I would prioritize the book for sure. As well the community of students and instructors on Ed and Discord was amazing.
On workload: Until week 5, I was averaging 19 hours per week on the course, but this number is skewed higher by working 30 hours on the 2nd week of A1. After that, I didn't keep track, but I would be surprised if I worked more than 15 hours on any given week outside the midterm week.
Rating: 5 / 5Difficulty: 3 / 5Workload: 15 hours / week
9YsRFGtC0IgtidFrG1xvDQ==2024-08-24T19:22:43Zsummer 2024
Machine LearningUltimately, this course ended up a mix of being extremely stressful and useful, but also fun. The assignments give a lot of freedom in how you approach each paper, and I found myself really immersed in trying to answer my hypotheses and analyze the results as best as I could. Oftentimes, I'd work 5-8 hours after work on the assignments because I was so curious.
But time management is also a skill that this course forces you to get better at by proxy; The assignments are quite open, so it required me to learn how to plan the papers such that I wouldn't become overwhelmed. Setting realistic goals while keeping the hypotheses interesting, performing complete analyses under tight deadlines, and managing expectations (keeping on track) are some of the extra skills I got the chance to practice.
The staff was super useful. TA's often led the office hours, but professor LaGrow was also present in most (all?) of them. I really encourage future students to take advantage of those OH, they're great and you get to tap in the knowledge and wisdom of great minds!
Lecture... some will like them, so won't. I had already been exposed to the algorithms taught in the course under mathematical approaches, and the lectures were just a lot of fun for me, and also a great way to see these topics under a more intuitive and humorous approach, rather than mathematical and dry. The book, and recommended papers, were there for more in-depth understanding.
In terms of workload, the Summer term was adjusted to reflect the condensed schedule and I found the changes fair. I managed to have a successful time in the course while taking an 8 days vacation and 2-3 more weekends off, but I would recommend (for sanity reasons) not to take so much time off (at least in the Summer), since I had to do a lot on week days, after full time work. I put 26 hours per week on average, it probably varied between 20 and 30, but I also had a big appetite on doing my papers and spent a lot of time on them.
Overall, a difficult course, but fun and extremely useful/applicable! Also for context, I ended up with an A in the course.
Rating: 5 / 5Difficulty: 5 / 5Workload: 26 hours / week
GVOAUZmYRwF+E367OR4ehA==2024-08-24T15:46:04Zsummer 2024
Special Topics: Applied Natural Language ProcessingThis was my 8th class in OMSCS.
I highly recommend this course to anyone in OMSCS or OMSA. It will change your understanding of the NLP industry.
Dr. Riedl is in my top 3 favorite OMSCS professors. He takes incredibly complex topics and breaks them into very learnable chunks. He does an incredible job summarizing the evolution of 50 years of NLP research from its origins to the modern day transformer architectures like GPT and BERT.
Many reviews give this a low rating on difficulty. This is mostly because the assignments cover complex concepts and coding frameworks that are already built for you. You are only required to complete a few simple blocks of code that allow the jupyter notebook to run. If you actually spend time walking through the provided code it, it is a way to quickly learn pytorch without too much pain.
What other reviews don't explain - maybe this is new in the summer 2024 class - is that the final project is actually really quite hard. You spent 80% of the semester expecting easy 4-5 hr assignments, but the final project is a 20-40 hr curveball. You are expected to design a KVMNet from scratch. KVMNets are early primitive precursors to the complex general purpose transformers like GPT. You train the KVMNet to answer questions about politicians based on a large Wikipedia dataset.
I didnt realize it until the end, but the KVMNet perfectly combines the principles you learned along the semester and provides a simplified format to understand the pivotal concept that allows GPT to work: attention mechanisms.
My only criticism is an echo of many other reviews in this thread - the lectures provided by Facebook are pretty hard to follow. You spend the first 3/4 of the class expecting best-in-class lectures from Dr Reidl, only to become frustrated at the end of the semester due to the Facebook lecturers reading off slides in broken English. There are some exceptions, I liked the 2 Facebook lecturers who came from GaTech - they seemed to be a bit better at developing educational content.
Rating: 5 / 5Difficulty: 3 / 5Workload: 10 hours / week
DRhNRbP80f8K8JAh8aMKfQ==2024-08-23T19:26:08Zsummer 2024
Introduction to Graduate AlgorithmsSell your soul. Realize GT is one of top universities. Its not easy yes bear the crown.
USE AI to help you study. if you don't really have time to study that much.
this is not just leetcode easy or medium problem you saw from tech interview. nor IQ test from some startup companies.
For exam. you have to absorb everything from lecture. your thought process have to be same with what lecture says to write solution.
the reason why exam problems are confusing is. they want to keep it in black box. like unknown coding test problem.
but still hard to believe since omscs program is designed for full time worker.
Happy to pass this class. just wanna take a rest for years
Rating: 4 / 5Difficulty: 5 / 5Workload: 30 hours / week
5ay3Qq60XSe2KK3VgvNJGQ==2024-08-23T13:28:33Zsummer 2024
Network Science: Methods and ApplicationsI thought this course was excellent. The material was interesting, the video lessons clear, the assignments enjoyable, and the TAs helpful. It's hard to imagine how an online course could be better.
Rating: 5 / 5Difficulty: 3 / 5Workload: 20 hours / week
vjTAghAYVf8qQ3jSGLg+fA==2024-08-20T13:19:43Zspring 2024
Introduction to Graduate AlgorithmsI don't recommend this course, the grading is too strict & inconsistent. Solutions are not gone through properly & fully, do post more post-homework/exam solutions so that students can learn better
Some exam questions were poorly worded causing confusion, and it's such a big percentage of the grade as well
Rating: 1 / 5Difficulty: 1 / 5Workload: 20 hours / week
QaHiGrgd+Pjfq59R17SqTA==2024-08-19T06:57:42Zsummer 2024
Introduction to Graduate AlgorithmsI would gladly take it, but I would consider not taking it if I knew Jamie was going to be a TA.
That would be a personal attack too like how you critiqued another reviewer on Brito's heritage.
Rating: 1 / 5Difficulty: 5 / 5Workload: 40 hours / week
o6S8rstKct8Vt6oBmGAUKA==2024-08-18T02:02:20Zfall 2023
Introduction to Information SecurityTerrible way to learn information security. Just try out hack the box or try hack me instead.
Rating: 1 / 5Difficulty: 3 / 5Workload: 10 hours / week
kyBBwyVo2ACVhQmYBPkoVA==2024-08-17T19:21:34Zsummer 2024
Computer NetworksThis was a great summer class. Light workload, but you still learn some interesting things. The projects are fun and relatively simple.
The course was at its best when teaching general and fundamental topics – like the principles of TCP communication or how video streaming works – and at its worst when discussing some obscure aspect of BGP that I'll never hear about again (e.g. ARTEMIS).
Would highly recommend using the Kurose lectures as a supplement to improve your understanding.
Rating: 3 / 5Difficulty: 2 / 5Workload: 5 hours / week
sO8OJlQ/P8sVDM5eftGHRA==2024-08-17T19:10:57Zsummer 2024
Computer NetworksI rated this course a 3 in difficultly mostly because the time constraints of the summer semester. I think they must drop a project for summer? All I know is this would probably be an easy 2/5 during the regular semester. I only spent about 20 to 30 hours on the projects and about 6 to 8 hours of studying for exams. It's not a difficult course, and the readings can be interesting and informative, but I wouldn't bother watching the videos. Ended with a low A mostly because I couldn't get 100 on the first project, and I completely forgot to turn in one of the lesson quizzes (cost me about 1.5 grade points). I got approximately 80 on both exams.
The projects were lackluster for the most part. If you're good at leetcode you'll probably find the projects easy. They're less about knowing networking and more about knowing how to find an algorithm. The only one that I thought required some networking knowledge was the firewall. I'd have liked something along those lines for the other projects rather than "here's a data structure, parse the data and return such and such values in this format". FYI, you will spend at least 2 to 3 hours trying to understand what the project is asking of you.
Overall, I thought the course was decent. Definitely not a great course, but I did learn some things from it so it wasn't a waste, but it could be better.
Rating: 3 / 5Difficulty: 3 / 5Workload: 10 hours / week
kcvYBtHvCTn3hwUoaXi/Fw==2024-08-17T16:32:31Zsummer 2024
Introduction to Graduate AlgorithmsFirst, I want to address the reviews that mention Dr. Brito's heritage/nationality. That's bullshit, irrelevant, and undermines the rest of your review.
There are very real things to criticize him for, like lack of involvement in Ed discussion, even when prompted by the TAs.
I don't like the pacing of the course. Assignments were released one at a time every week, so it was impossible to get ahead of schedule in the class if you needed that kind of flexibility. I didn't need that this semester, but at other times it would have been frustrating.
Jamie McPeek needs to stop being a TA. I can't remember a single time where one of his answers actually provided useful information. It was mostly memes and taunting of students that missed something while reading a course post or syllabus. The rest of the TAs were quite professional and helpful. If I knew they were TAs on another course I would gladly take it, but I would consider not taking it if I knew Jamie was going to be a TA.
The grading was inconsistent and sometimes overly harsh. My homework scores barely seemed to correlate to my test scores, and all of my scores varied greatly. I got 100% on a test in one section and then about 65% on another section, and I don't think my understanding of the material was that different between the two sections. As further proof of the inconsistent grading, there are posts for every assignment where students are expected to discuss whether deductions on their assignments were valid, or if they should request the TAs try to grade them again. I passed this class on the first try -- I can't imagine the frustration of having to take it twice, knowing it was probably due to who graded your homework and how much coffee they had on the morning they graded it.
A lot of students complain about the writing, but I don't think that's the problem with this class. The homeworks are basically writing proofs for some approach to solve a problem, which is different from the coding assignments we have in a lot of other classes, but fundamentally just as important for a computer science education.
In summary, the material for this class is really fun, but the inconsistent grading and unprofessional behavior from one of the TAs make it much more stressful than it should be. It's good for Georgia Tech that it's the last class, because if it were the first, a lot of students would get a degree elsewhere.
Rating: 1 / 5Difficulty: 3 / 5Workload: 20 hours / week
QaHiGrgd+Pjfq59R17SqTA==2024-08-17T00:21:28Zsummer 2024
Introduction to Graduate AlgorithmsIf you don't really worry about the difference between an A and a B, because of how strict Dr. Communist Brito is about grades (12.2% A is a joke on this class, seriously), you will enjoy this class.
If you're thinking only about A (which you shouldn't), this class will be a massive clusterf**k.
Rating: 4 / 5Difficulty: 5 / 5Workload: 36 hours / week
wyjpNtGgqFkXmoaZYuL+nw==2024-08-16T13:36:28Zsummer 2024
High-Performance Computer ArchitectureDecent class where you do learn some concepts well, but I regret taking it and this is my first time compelled to write a review.
As someone who prefers "hands on" learning, this is course is extremely difficult to focus and learn. It predominately consists of watching dozens of several hour length lectures at a snail pace and doing little practice problems. The problems are like on paper theoretical representations of caches, etc. Some may enjoy this, but I dreaded having to sit and watch them and found it very difficult to pay attention (maybe that's just me). I'd recommend watching the coursera videos/videos on youtube and deciding whether that's your type of thing.
The projects aren't enjoyable, hours and hours of just trying to tweak some code. And the final is so heavily weighted and unlike the practice exams that going from an A to C from the final is not unlikely. The TA is very helpful throughout the course, but don't expect any help on regrades.
If you are someone who has a full time job, and are taking these courses to casually learn on the side--I would stay away. If you do take it...well, take good notes on paper since you can use those on exams.
Rating: 1 / 5Difficulty: 5 / 5Workload: 25 hours / week
4Oabk5zw+1/cNQe1QOdbTg==2024-08-15T21:26:40Zspring 2024
Software Development ProcessI took SDP as my first class. I think it’s a perfect start for people like me who had no CS education background or related work experience and taught ourselves coding. The course was easy with small challenges. It was well designed. I learned git, design process, and testing. The group project, which was designing a simple Android app, gave me the opportunity to work in a team and got a taste of both frontend and backend development. Grading was also great. Almost all assignments were submitted to GS with unlimited submissions, no hidden cases, and immediate release of grade. Generous extra credits were provided.
Rating: 5 / 5Difficulty: 3 / 5Workload: 12 hours / week
MBQeCEa32L2sc36g8XNAEg==2024-08-15T19:07:00Zsummer 2024
Data Analytics and SecurityOverall, this was a very easy course that is heavily backloaded with the final paper. Most of the assignments are very easy and will not take any time. The quizzes are also very simple as the questions are mostly taken directly from the lectures. The final paper/presentation could be very time consuming depending on your group, but as long as you start early when the project comes out it should not be that bad.
Rating: 2 / 5Difficulty: 2 / 5Workload: 6 hours / week
43Z2ccpL9BF2RYUXKcwKuw==2024-08-15T17:15:17Zspring 2024
Software Development ProcessThis course is pretty good until assignment 6. As most people in previous reviews said it teaches a lot about the SDLC regardless if you are already a SWE or not. The group project and every assignment until assignment 6 isn't difficult and can be done in about 10 hours or so a week. Maybe less depending on your experience. Try to stay on top of the group project because it is pretty easy to get "bad" teammates (bad teammates imo are people who either don't know much AND aren't willing to learn or teammates who don't contribute at all).
So here we go with the rant. Assignment 6 is the worst. It's not because of the assignment itself and the goals of the assignment. It is a terrible assignment because of how poorly it is written. Asking questions about A6 on the discussion forums doesn't help either because the TAs are genuinely useless and give the most vague answers.
If you are a TA or professor for this course, you should be deeply ashamed of yourself for how pathetic of an assignment you have created. Don't be a TA if you're going to give useless replies to questions or not even respond. After everyone received their grades (which is whole other issue, it takes weeks to just grade assignments when there is an autograder. It doesn't even make sense for how long it takes to grade and input grades/scores), the forums were up in flames. There were at least a dozen or so posts about A6 regarding the poor grading and questions that A6 asked. So what did the TAs or professors do? Nothing. They simply replied "sorry" and were never to be seen again. I, like many others in my class, went from a high A (97+) to a high B (87-89) with just 1 assignment. The worst part is that A6 is only a week only assignment and is worth the most, yet the group project which goes on for almost 2 months is barely worth anything.
If you can avoid taking this class, do it. It's actually a disgrace for this class to be in a great program such as OMSCS.
Rating: 3 / 5Difficulty: 3 / 5Workload: 10 hours / week
mhCfn5wF3DOFzGlaGSYD+A==2024-08-15T07:39:17Zsummer 2024
Deep LearningVery interesting topics and sets up a good foundation for those pursuing ML specialization. However the course is very demanding and requires at least 20 hours per week. The assignments and quizzes are quite daunting. The assignments take weeks to complete and are not easy at all, leaving very little time to prep for the quizzes. The project and the theory parts of the assignments was graded leniently though.
Rating: 4 / 5Difficulty: 5 / 5Workload: 21 hours / week
3eXrkhR5BL7TZ13Hm7YIHw==2024-08-15T00:02:29Zsummer 2024
Introduction to Graduate AlgorithmsEverything that needs to be said about this class has already been said at this point.
The material is essential for a Computer Science degree. It is a class worth taking, despite its numerous problems. Based only on the material, this course is easy to moderately difficult, depending on your background.
The course itself is poorly organized, the graders are pedantic, and overall it's hard to see how the current iteration of the course is worthy of Georgia Tech. This causes a level of stress that is unjustified by the material. Despite this, you will be successful if you put in the effort, ignore the noise, and work at it.
Rating: 1 / 5Difficulty: 3 / 5Workload: 20 hours / week
QaHiGrgd+Pjfq59R17SqTA==2024-08-14T23:58:00Zsummer 2024
Introduction to Graduate AlgorithmsAvoid Leetcode
It is apparent from one of the HWs that one of the question was structured such that it is similar to a Leetcode question that people who have memorised got trapped into answering it directly and got sent to OSI for cheating.
Fair enough, because it's a memorised code and not something that a student came up with on their own.
Rating: 2 / 5Difficulty: 5 / 5Workload: 30 hours / week
Rp+/iLY5IxXNMmn7CwtrJQ==2024-08-14T15:40:26Zsummer 2024
Introduction to Graduate AlgorithmsThis is a tough class. Partly because it is not structured the way other OMSCS classes are, many of us are usually taking this as the final class or one of the later classes. This was my 8th class. I struggled a lot adjusting to how the quizzes and homework feedback should be incorporated into your exams and assignments and was way behind early in the semester (My grades were 48% at end of HW4 and 55% on withdraw date, glad I stuck through and finished with a B).Only in the later half post exam 1, I really did figure out how the grading worked and what I needed to do to sail through. Also grading feedback takes its time, for example feedback for homework 1 came back after homework 2 submission deadline was over. So, I had points deducted for similar mistakes on both the homeworks and couldn't really incorporate the feedback for some of the assignments. For regrades, there is a bureaucratic process where you first need to elicit feedback for a regrade request from your fellow students and then make an official request. This was so inconsistent where for a same kind of issue some students agreed that it needs to be regraded and some did not. Programming assignments were introduced for this class, what was frustrating was students are not allowed to share their test cases. This is unlike many other courses for example HPCA or GIOS where it is allowed and encouraged to share testing framework. I do not agree with the TA's justification that the test cases are part of the understanding process for the algorithm design. Most of the cases in gradescope which were used to dock the points were fringe edge cases. Exam problems by themselves are not that hard as long as you interpret them the way TA's intended to :), which was a problem this semester. Being used to working in a professional courteous environment where written communication with colleagues is very formal and polite, it sometimes gets irritating when some of the TAs don't answer a straight forward question asked on the forums but reply with mocking undertones which would potentially be cases to report as abuse in my company. Shoutout to Joves for taking the time out for exam study prep, without which I wouldn't have passed this course. Also to Aja for the post encouraging people to stay on course after exam1, I was considering withdrawing and decided against. Overall I did still enjoy the course material, solving the problems on the exam and the ones behind the textbook. Dont be scared, put in the time and learn how to solve the problems in the textbook on your own you will sail through.
Rating: 4 / 5Difficulty: 4 / 5Workload: 15 hours / week
xinG+hsBQH7n5OmfykHSHQ==2024-08-14T13:00:03Zsummer 2024
Special Topics: Financial ModelingI don't understand the hate for this course. Sure it is not a programming class and it never pretended to be. Some may dislike Garner's approach to handholding of going through step by step in every Excel cell but I don't think this was the point of it.
Learning how to read and understand financial statements requires a different modality of thinking to learning a programming language, library, or framework. As such, it requires a different approach to learning the content and I think Garner did an amazing work with this.
The material itself is dry, but it has nothing to do with the quality of the class but rather the subject matter in itself. Having this understanding will be helpful in the long run of your career.
Rating: 4 / 5Difficulty: 2 / 5Workload: 8 hours / week
f3jgHHOzhTaeyZL4My9S9Q==2024-08-13T02:12:57Zsummer 2024
Introduction to Graduate AlgorithmsI got an A on this course. First time to take the course. Glad that I graduate.
** I dont have job so I spent about 50 hours on this course every week**
The lecture videos are really intuitive and I truly enjoyed.
Dr. Brito is nice. He frequently held office hours and he listened to students' advice/complaints on the exam and gave them points back.
Shout out to Joves!! Great man willing to spare his own time to held marathon office hours for students even when he was not the TA this semester. I wish he was the head TA in summer semester. Kudos to him!
However, I really had a very very stressful and anxious semester. I felt this was the most pressure semester that I had in OMSCS program even when just submitting a homework.
Three things that make me feel sad about the course:
- TAs. I dont want to name some specific people and complain. I feel there were good TAs that were responsible and willing to help, like Tim W. However some TAs really didnt help at all. They often avoided to answer the questions like by reiterating students' questions in a sarcastic tone which is really condescending and made me feel so sad for the student
- I was flagged in the last HW. I dont need to do that HW to get an A, and I just wanted to practice my coding. One of the questions in this HW was from leetcode. I CITED the leetcode question, but still got flagged. Really upset to end my OMSCS journey in this way.
- Assignments. They used Gradescope for coding HWs. Only simplest test cases were given to students, and they had around 20 or more hidden cases. It understandable to have hidden cases, but I feel students were only notified "you got 4/20 in this HW" which did not help anything at all. We cannot really learn from the coding hws. They could have mentioned like "this type of corner cases is worth noticing", but not even one. Our purpose is learning, not just the grades.
In general, I truly hope the course could get improved since most of OMSCS students need to take this course as the last course before graduation. It shouldnt be a bitter ending like this. I dont know if someone from the department could see my comment and help with the course. If not, for all students, try to avoid it or cultivate a strong heart to prepare for it.
Rating: 2 / 5Difficulty: 4 / 5Workload: 50 hours / week
QaHiGrgd+Pjfq59R17SqTA==2024-08-13T01:15:26Zsummer 2024
Introduction to Graduate AlgorithmsI don't have beef with the TAs and how they're run (in fact I did agree with some badass things Jamie did to some of the students) but how invisible Brito is with his f&*%ed up Exams (which everyone had seen on Exam 2), his weird grading regimes, and his unwillingness to work with students on a sensible path forward, despite constant reminders by the TAs to address student's concerns.
OMSCS had been fun until this last course which I had to clear. Really makes a salty leaving from the entire programme which I am really ambivalent on recommending to others - not that they needed any more introduction these days.
To that effect, I have decided to suspend my annual donation to the College of Computing OMSCS Fellowship Fund. The huge % of W grades in this semester is enough to buff up their coffers anyway.
Rating: 2 / 5Difficulty: 5 / 5Workload: 38 hours / week
ev5gl2M7ax2eq71AhVNiEg==2024-08-13T00:21:53Zsummer 2024
Introduction to Information SecurityFor awareness, I am a CY policy track student. I did my best in advance to meet the prerequisites. They do well posting the prerequisite material in advance however the material is quite broad and varied so it is quite difficult to effectively prepare.
The summer course has 9 projects in 11 weeks, roughly a week per project. I cannot give the TAs any grief for increasing the project amount vs. previous summer semesters, that is because Georgia Tech writ large requires summer courses to have a condensed timeline with the same course expectations as a typical semester.
This course was challenging for many reasons.
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This course is project-based so there are no real lectures, mainly office hours to go over the project. The rules clearly state you cannot work with another student on the project, cannot hire a tutor, you must work on the project by yourself, or ask the TAs for help which is not an entirely unfair expectation however, there are a myriad of variables that can cause problems and this approach does not encourage data retention.
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The VM, I understand why a VM is used, however this variable makes it difficult to pinpoint what is going wrong. For instance, if the student is not receiving the expected results, it could be user error (e.g. problem with the code), the container is having issues and needs to be reset, the VM is not behaving correctly, etc. This happened to me many times, I had to re-download the VM 4 separate times because it would go so slow I couldn't type out a single line of code in the terminal and this was after I scoured troubleshooting videos and spoke to a TA only to be informed that while they acknowledged my issues and they were highly unusual, no extension would be granted (I didn't ask for one).
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Trying to do too much for a broad group of people. If I understand it correctly, this class is mandatory for CS and CY grad students. These students are on very different playing fields and it shows. Half the class aces it, as they should with their background, and are left bored. The other half struggles consistently. I routinely put in 40ish hours of study each week. The focus is lost with the variation of subject matter. These 9 projects are well-crafted and I give kudos to their creators, however they cover areas ranging from Java, Python, cURL, malware, network packet analysis, machine learning, web vulnerabilities, binary exploitation, etc. I have taught courses for the better part of 10 years and I'm not sure what objective this course is meant to achieve other than testing the limitations of its students and their ability to quickly learn a complicated topic in a short amount of time. However, the audience is highly uneven. Recommendations: I'd recommend splitting the course between CS and CY students, keeping it the same for CS and for CY splitting it up into 4 modules, with 4 core group or individual projects with different variables, some CTF, some quiz, with half the projects focused on exploitation and the other 2 on defense. I was so fire hosed with information it is difficult to recall most of what I learned. I would have appreciated a more nuanced understanding of operational information security as it relates to cybersecurity. Also, please drop Intro from the title, that is an oxymoron for a graduate-level class, there is nothing introductory about a course when success hinges on prerequisite knowledge.
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It truly is a fun course, but I can say that now after I've passed, barely. I spent 10 years in the military, work as a defense engineer, have an undergrad in CIS, and have programming experience, and I have never been more overwhelmed in a class. I tried my best to research on my own, read through Ed Discussion, watch office hours, and every week, on top of my 50 hour work week, I was spending 40 hours on these projects. I felt like a failure most days. It's doable but understand what you're walking into. You will feel alone, you will feel lost, you will have to teach yourself a lot. This is expected. Also, effectively communicate where you are struggling, that makes the difference in the help you receive from the TAs.
Anyway, toodles. Have fun future folks.
Rating: 2 / 5Difficulty: 4 / 5Workload: 35 hours / week
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f0vxGbMqufBjspxM8mlg/Q==2024-08-12T22:24:38Zsummer 2024
Software Development ProcessRelatively easy course (coming from soft dev background)
- Assignments and individual projects are okay
- Group project could be tricky because of team dynamics. Overall, easy to get an A
Rating: 4 / 5Difficulty: 3 / 5Workload: 10 hours / week
riOMki32eNBpb+1jBCtpOQ==2024-08-12T20:28:13Zspring 2024
Database Systems Concepts and DesignA relatively easy class but grading is not that good. Easy B but difficult to get an A (myself and a few friends I know got 89.xx and they will NOT round up).
The final exam will be really bad (average ~70), so be prepared to get almost full mark from all previous exams to leave enough margin for the final one.
The team project is a well defined full-stack website. You are free to pick the stacks you like. They dont care about your front end or back end as long as they are functioning well. TA will specifically check your DB outputs from the front end to ensure your DB SQL logics are correct.
I do recommend to attend the OH. Both prof and TA are really nice and willing to answer students' questions and help you succeed. The TA is like 24x7 online and your Ed post can be replied literally right away.
Rating: 3 / 5Difficulty: 3 / 5Workload: 8 hours / week
riOMki32eNBpb+1jBCtpOQ==2024-08-12T20:11:41Zsummer 2024
Video Game Design and ProgrammingStrongly recommend this class if you are interested in how to make video game in Unity. Basic Java/C# will be good enough. I got an A from this class.
The first 5 individual assignment will walk you thru the key features of Unity to build up a video game, then it will be up to your team to create your own game. The project is really open ended and a TA will be assigned to your team to play your game, listen to your presentation and give you detailed feedback on your game. Every team will deliver a 3D video game and a trailer in the end, and the trailer will be published for the entire team to watch. You can literally see that every team did an awesome job and created incredible game within a few months. Strongly recommend to check out the trailers from previous semesters when you start taking this class. That will give you an idea how great the final game you can make. Of course, it all depends on how much time and efford your team want to spend on this game, but it really paid back once you see your own game being built up.
Rating: 5 / 5Difficulty: 2 / 5Workload: 20 hours / week
riOMki32eNBpb+1jBCtpOQ==2024-08-12T19:56:50Zspring 2024
Software Architecture and DesignI took this class in 2024 spring as the first class in OMSCS. As mentioned by some other review, this class was updated dramatically since 2024 spring. It used to be a super easy class, but it's not the case anymore (from some aspect). Pros: - The group project is to do a full-stack website for Pokemon battle. This topic is pretty interesting. You have full flexibility to pick your own frameworks, DB. Pretty good chance to practice every aspect of the full-stack dev. - Grading is not bad. I got an A eventually (I did spend a lot of time on this course though). Especially the final project phase. I really doubt they don’t have enough time to grade everyone and ended up curved the grade. - Prof seems to really want students to learn something, so I guess that's part of the reason why they are trying to fully revise this class. Cons: - You will be really burned out by the messy and disorganized arrangement of this class. The assignment won't clearly tell you how they will test your code, which is really frustrating. TA will tell you something like 'we will test your code with our hidden Pokemon' without either sharing one of their hidden pokemon, or sharing the interface attributes how his hidden pokemon was set up… We will have to guess and wish we guess correctly, which is ridiculous. The released assignment PDF and starter code will be revised many times after publishing, and they will never shout out. You'll have to consistently check back to ensure you are still working on the latest version (I learned this in a hard way to debug for the entire night and eventually figured out that they revised the assignment PDF without publishing an announcement). The exams are super subjective and open ended, so it's really difficult to prepare for it and know exactly what you should write in the answer. - Many critical info, like changes to assignment, how the exam will be tested and graded, how your code output should look like, will be splattered among tons of Ed post answers and 2-3 hrs office hours/week (and yes, weekly OH is mandatory for this class). - Ed is really busy for this course as all students are posting actively to clarify what's required for assignment/exam, which made browsing the critical info that TA posted even harder. In our class, there was one student eventually volunteering to summarize all the critical info from Ed and OH for the entire class to track, which is supposed to be done by the TA team (and you can also imagine how messy it was). - TA will never answer the question straight. He always answers a question with another question, or something like 'I believe/I think'. It turns to be a guess game. Since there are many info that are really not clear for everyone, people tends to ask similar questions from everywhere, and TA will just answer something like 'this was answered in the office hour' without mentioning which date, time frame you should check among 2-3 hrs OH video per week. - As many other courses, the group project is a big part of this class, and you really want to make sure you have a good team (unfortunately this part is really out of control. My teammates didn't involve that much and I ended up having to do most of the project all by myself). What makes it even worse is, since so many students were disappointed with this course, there ended up have many people drop off the class after midterm, which made the rest of the teammates even more struggling. Prof. and TA did ask the teams to re-hire people as needed, but it's not an easy thing to have a new guy joining your team after midterm and take over the group project code from middle.Anyways, this class might be getting better after a few semesters assuming they will listen to all the feedback from students and settle down with the assignment/exam/grading and understand what they really want.
Rating: 2 / 5Difficulty: 2 / 5Workload: 30 hours / week
v1v10dr1IPSgSfNc76EumQ==2024-08-12T19:30:44Zsummer 2024
Software Architecture and DesignWhat could have been, and should have been, one of the best and most important courses in OMSCS ended up being a huge failure.
This course has so much potential to teach students the most relevant and industry-related concepts and principles of Software Engineering. However, it feels stuck in the 2000s. There's an excessive focus on UML diagramming, with four weeks of lectures dedicated to it. Additionally, the course feels like a graduate writing course due to the overwhelming number of writing assignments. The course structure is chaotic, with too many components running simultaneously, leading to distractions. Assignment quizzes are based on books and papers outside the course lectures, requiring substantial time to properly learn the material. On top of that, there are project assignments, exams, and lecture quizzes, some of which even test your memory in trivial ways (e.g., how many windows are in a building from a lecture, not an actual question but to emphasize the point). The lectures themselves are outdated, extremely long and difficult to navigate. Furthermore, a portion of the grade depends on viewing all posts on Ed/Canvas and attending/watching mandatory weekly hour-long office hours. I genuinely want one of the course TAs to take this course and see if they can manage everything thrown at them, and have a successful learning outcome. Critical project-related clarifications are buried in minute details sprinkled around Ed comments, which is frustrating.
The most disheartening part of this class was never receiving final grades or feedback on the group project that my team and I worked on tirelessly for three weeks, sacrificing our weekends. It feels irresponsible and disrespectful of the students' time. As another review mentioned, it's hard to believe Georgia Tech is allowing such a dysfunctional course to be offered.
As someone with 8 years of experience in the software industry, I was particularly disappointed to see how disconnected this course is from the realities of modern software engineering. If the course creators genuinely care about teaching industry-relevant concepts, I would suggest the following improvements:
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Condense the UML portion of the course to one or two weeks: The industry has largely moved on from the traditional use of UML, and the course is not keeping pace with current practices. Instead, the course should focus on core skills like Architecture and Design Patterns, which are essential for any software developer.
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Focus on meaningful learning: What does a student gain by answering how many items are in a specific object from a lecture? Please design quiz questions with the student's learning experience in mind. We are here to learn engineering concepts, not to improve our memory.
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Prioritize quality over quantity: Be realistic about what can be accomplished in 11-15 weeks. Due to the overwhelming number of tasks, my group and I could not focus on the lectures and actual learning. We were constantly distracted by all the additional tasks, which defeats the purpose of the course. Mandatory office hours and Ed discussions are not conducive to learning when we are already swamped with other course requirements.
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Be timely and respectful with feedback: We spent countless days on the final project, and we deserve to know how we performed. It’s incredibly frustrating that we received our final grades without ever getting any feedback on our group project. Despite all the hard work, we never got to know how we actually did. Additionally, we provided detailed feedback on our teammates for the first half of the project but received nothing in return, which could have helped us improve for the second half. Even more frustratingly, we were asked to provide similarly detailed feedback for the second half, but once again, we got no feedback at all. If feedback isn’t going to be provided, why ask us to invest so much time in it?
Overall, as I mentioned at the beginning, I enrolled in this course to learn industry-relevant concepts. When I first saw the course content in week one, I was excited and felt I had made the right choice. However, as the course progressed, I was devastated by how poorly it was executed. It was a complete waste of a course, money, and time. Totally unacceptable.
Rating: 1 / 5Difficulty: 3 / 5Workload: 25 hours / week
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Bi1d9Zzy/nPldLgkW5hvug==2024-08-12T18:51:00Zsummer 2024
Deep LearningPrevious courses: KBAI, Simulation Grade: A
This is my third course in OMSCS program and I enjoyed it a lot. I've been working in data science for 2 years so the math part for this class wasn't too hard for me. The first assignment did scare me a little because we had to code neural networks from scratch using numpy, which is something I've never done before. The assignments do get easier though because we are allowed to use pytorch for the rest of the assignments. The professor did a great job explaining the concepts and the implementations. The TA hours are extremely helpful too, especially if you feel stuck on certain part of the assignment. Overall, this is my favorite course among the three I've taken.
One thing I want to complain is the course video quality taught by the Meta team. It was really really bad. It was hard to follow, especially in the lesson for word embedding. They just read the slide scripts and they can not even read them fluently. I understand that the Meta team is specialized in conducting research, not teaching. But I would rather this part of the course to be taught by someone who's less knowledge in the field but better at teaching people the concepts and using examples to help students learn better.
Rating: 5 / 5Difficulty: 4 / 5Workload: 12 hours / week
9kzcfiS2THy16C9a+/vGVA==2024-08-12T04:33:14Zsummer 2024
Statistical Modeling and Regression AnalysisI took this course in the summer of 2024. My main suggestion is to change the grade weightage by giving more importance to the four homework assignments. This would really help reduce the pressure to perform well on just the two exams. Also, I have to mention that some of my classmates were really disrespectful to Dr. Serban and the TAs, which made the class pretty unbearable for those of us who were trying to focus and learn. Below are some of the comments:
"Amazing. We are a technology school and we have to grade the papers by hand. Absolutely NUTS. This class is the peak of amateur hour. COMPLETE JOKE"
"Cry me a river - the instructor team signed up for the job. If they didn't want to spend their summer reviewing honorlock videos that's on them."
"Since the TAs took it and some managed it in under 75 minutes when can the videos of their session be released so we can see how they better organized themselves than we managed to?"
"I am literally hoping summer to end now because of this nonsense !"
"Very well pointed out an example of a good and sensibly structured exam: CSE604"
"Yeah total bullshit."
"Pathetic timelines for the exam"
"Thats annoying AF!"
"They definitely are - their silence speaks volumes."
"Academia is littered with educators like this and make it unattractive to prospective students"
So do we really have to come on this forum to bash the exam and course again? "Yes I would hope so. This forum is setup to …….check notes….. discuss the class and exams!"
"I included have sometimes been unprofessional in my responses here."
"one of the threads too about some BS etiquette and using that to possibly remove students from the course."
"I request considering the option of requesting additional resources from the ISYE department to meet this minimum educational responsibility."
"Kindly ensure that we do not receive incorrect peer review grades because of inadvertent errors by the evaluators because of "not easy to use" format."
Rating: 3 / 5Difficulty: 3 / 5Workload: 12 hours / week
lvbpBJESbe2FyVHsecRLUg==2024-08-12T02:45:14Zsummer 2024
Data Analytics and SecurityThe previous reviews were still true in Summer 2024. I'm providing this review for the purpose of providing tips for students who are more like me. I do not have a traditional CS Bachelor's background. This is my third class in the OMSCS program. I took this class as an introduction to learning about the security. Some, maybe majority of the, students in the section were in OMS in Cybersecurity.
The course lectures introduced a variety of topics which were new to me. However, I did not feel the learning experiences designed were helpful for me to internalize what was taught. The quizzes were on the lectures, but there was no expectation of thinking because all quiz questions could be answered by searching the slides. In fact, the response could be marked wrong if some thoughts were put into understanding the lecture.
There were two projects to choose from. Signing the nondisclosure agreement (NDA) did not limit your choice to that project. You could sign it early and get the data before you decide which project you would like to choose. NDA can be signed as soon as the semester starts. I had never worked with JSON files before so I appreciate my project experience in this class. I also suggest view the last two modules for the project, each had information on one project. I also felt the details expected on the midterm/proposal were to the level of a completed project rather than a proposal.
The instructor had weekly office hours. Some students got specific help and even code snippets, but some other students would not get a response other than "I don't know," depending on the question.
I felt the execution of the class was poor. The Canvas course material was made available throughout the semester instead of at the beginning, which I did not like. There were out-dated instructions and links in the course material. Students may ask questions on Ed and the response would be different from what was in Canvas. I would suggest reading Ed posts often.
Some of my expectations as a student (stated in XXI. Student-Faculty Expectations No. 8-10) were not met. I did not feel grading cretiria were clear or grades were posted in a timely manner. For example, I could get different number of points deducted for the same reason on different assignments of the same type. Out of xxxx graded assignments, xxxx of them had grades released outside of the two week window as stated in the syllabus. Note there was also a ceiling effect since there was a deadline to release grades set by the university. TAs could discuss what was sent in a private message in the public forum, which led me to wonder if they were trained sufficiently. Letter grades seemed not to be criterion-referenced because the conversion was only posted after all grades were posted. For Summer 2024, directly copied from the announcement was: A: 86% and above B: 76% - 87.9% C: 66% - 77.9%
As for TAs, all previous reviews touched on a lot of aspects, so I will not repeat much. One difference could be that this semester, they specifically pointed out that there were re-grading of assignments unless it was a setting problem on the auto-graded quizzes. This meant any mistakes made by the TAs would not ever be corrected. I suggest reading the instructions carefully, treating any seem-like suggested examples or guidelines as required, listing out each question separated by a question mark in the discussion prompt and response to the question, leaning on the upper limit of the word count, posting on Ed publicly with any questions, reading Ed posts, and making sure the one-sentence response is easy to find plus a lot of details.
Rating: 2 / 5Difficulty: 2 / 5Workload: 12 hours / week
cRXz1sFin0M1pGkQUS9Ykg==2024-08-11T20:09:44Zsummer 2024
Statistical Modeling and Regression AnalysisI was part of the summer 2024 section for this course, and if you've read past reviews both on here and on reddit, you'll see that was a bit of a roller coaster. While I definitely think there's room for improvement in this course, and many of the gripes are legitimate, I wouldn't necessarily let those reviews scare you away from taking it depending on your situation. If your goal is to learn regression at a deeper level, this course will definitely provide you that, and I found the workload very light compared to other courses which also may be appealing to some. However, if you're looking for an easy A and care heavily about your GPA, the exams may make that difficult for some.
Starting with the lectures, I didn't experience any issues with audio quality that others had mentioned. The professor has a thick accent, but I was mostly able to understand her and was able to refer to the transcripts if I couldn't make out a word. I also found the lectures to be pretty well organized/structured, as many of the modules follow a similar approach. Despite the chaos, it seemed many students on piazza agreed that they found the lectures informative. I will say that there are certain topics that she could explain better and did involve me having to google to understand it deeper, but I didn't find that to be a huge burden. Additionally, the amount of statistical properties thrown your way may be daunting at first, but it settles down, and you don't really need to understand the "Why" behind those properties to do well in the class. You can just write them down on your cheat sheet for the test questions on them. If you took simulation or the pre req statistics course you'll probably understand it better.
Another thing about this course that many don't seem to mention is the pacing. I took this in the summer and I still found the workload/pacing to be very light compared to other courses. There were four modules released every two weeks (three weeks for module 2), each with a homework. There was also no material released on exam weeks which was really appreciated. I was often able to get the videos and hw done in one week and then have the following week to either review the material or take a break from the course.
Then there's the exams, and this is where the course could really use some improvement. It's 40% theory and 60% coding and people had legitimate gripes with both sections. As many have mentioned, the wording of some of the multiple choice questions seem to be designed to trick you. You may understand the concept, but the way the question is worded may make it hard to follow what it's actually asking. I think these should be improved, although thankfully it's less than half your exam grade. Through reviewing the lectures and making a detailed cheat sheet, I was able to score in the upper 80s and 90s on the two multiple choice sections so I think it's doable with sufficient preparation, but still not the best way to test people IMO.
For the coding portion, there didn't seem to be issues in past semesters but for whatever reason, this semester these exams were also flawed. My biggest gripe is that they don't allow open internet on these, so if you don't know how to code something and it's either not in the lectures or just hard to find, you're kind of stuck, and then subsequent questions are affected. Coming into this class with some background in R would be useful to ease the stress of issues like that, but mostly all the code should be available in the notes. I think making it open internet like in CSE 6040 is the best route forward to address this concern as coding in the real word is also open internet. The big issue with the midterm was timing as many people didn't finish. I barely finished but if you had trouble with your code at any point I think it would've been real difficult to impossible to complete this in time. They seemed to recognize their mistake and gave everyone 5 points back, and additionally offered an open internet replacement midterm if you wanted. This is the period when piazza got pretty nasty between the professors/TAs and the students, as you'll see plenty of mention of it on here and reddit. For the final, I didn't find the timing to be an issue but this is where I experienced a coding issue that wasn't covered in lectures. There was a data type issue that needed to be debugged and again open internet would've been really valuable here. Another gripe people had was the material covered was harder, as an algorithm that was lightly covered in lecture, but never in homeworks, showed up. I personally think anything covered in lecture is fair game and that question could be solved by referencing the lecture code. The scores on the final were also very low and they ended up curving the whole class by 5% in addition to the 5% midterm bump (A is 85+, B is 75+, etc).
In summary, I think there are some improvements needed in the exams such as more fair multiple choice questions, and open internet coding exams with sufficient time offered, but you'll definitely come out of this course with a deeper understanding of a very important topic. My best advice is to ignore the chaos, study the lecture notes and code thoroughly enough so that you feel sufficiently comfortable, and you'll be able to do well. If you're super worried about your GPA and don't want to experience the exam stress, then I also understand why someone would skip this and learn it elsewhere, but I truly believe doing well is possible with the right preparation.
Rating: 3 / 5Difficulty: 3 / 5Workload: 7 hours / week
cRXz1sFin0M1pGkQUS9Ykg==2024-08-11T16:44:45Zspring 2024
Data and Visual AnalyticsI really wanted to like this course as it covers some really important topics in the world of data science which could really set this program apart from others, but I feel it fell short. Your experience in this course may also be heavily influenced by the group you end up with for the project so make sure to get on top of that right when the course begins.
Some of the material covered was awesome, such as data collection, cleaning, and integration as well as sql and scaleable computing. The course also covers data visualization which again could be a very valuable topic, however, instead of things used in the industry such as tableau, power BI, and python visualization packages, you go in depth on D3 which just felt like a huge waste of time. Additionally, the last few weeks of the course along with the last homework covers modeling topics that are taught in more depth in other courses, such as tree based models and clustering, so I didn't really understand the point of those. I felt this time could've better been used covering the more relevant data and visualization topics I mentioned previously.
Another issue is the lectures don't add value in this class and can be skipped aside from the bonus quizzes. They're very high level, only provide a short overview of the topics, and aren't connected to the assignments we're graded on. I do value that the class forces you to learn code on the fly as that's similar to what you experience in the real world, but when it comes to education, applying and reinforcing the lecture material in some way is the best way to retain and learn information, and this class falls short on that.
As for the assignments, they're definitely long and the difficulty of them depends on your background. Homeworks 1,3, and 4 are more python/sql based so how long it takes you definitely depends on your level of python proficiency. If CSE 6040 was easy for you, you probably won't struggle too much on these so YMMV here. On the other hand, homework 2, the D3 assignment was the nightmare it was expected to be, and the fact that most of us will probably never need D3 makes this even more frustrating. As others have mentioned, you can score very low on this assignment and still get an A in the course, so that may be the move for you if you don't want to devote a crazy amount of time to it. I also found coding the random forest from scratch in homework 4 difficult, but if you're comfortable with OOP and have taken ISYE 6740, you may not find it challenging.
Lastly, there's the project worth half your grade. As others have mentioned, it's graded pretty leniently and your experience on it really depends on how good your group is. Generally, the people who'll be the best teammates are the ones who are actively looking for groups on the first day of the course, so I recommend being active on ED from the get go to find people. If you follow all the guidelines, it's graded pretty leniently so you pretty much get out of this project what you put in. You can be ambitious and end up with a project that you can advertise in job interviews, or you can do something small and still get an A. Again, YMMV depending on the group you end up with and how complex of a project you want to go for. Just be prepared to work on it throughout the semester to meet the deadlines instead of cramming, and if you end up with a bad group, be prepared to do a lot of heavy lifting. I'm honestly mixed on how worthwhile I think the project is. On the one hand, it gives you the opportunity to really push yourself and apply some of the material covered in this class, but on the flip side, the guidelines are so open ended that you may also not get a whole lot out of it and may have to deal with some online group project chaos.
This class is so close to being great if it just makes some tweaks on how it operates, but despite how challenging and time consuming it may be for many, it's also not difficult to get an A due to the unlimited assignment submissions, lenient project grading, and bonus quiz opportunities.
Rating: 2 / 5Difficulty: 4 / 5Workload: 14 hours / week
mhJ6z5HGo+fWK/6onWIplA==2024-08-11T13:41:02Zsummer 2024
Human-Computer InteractionFinished this class with some bittersweet taste. On the one hand, the contents are incredibly interesting, and the videos are well-done. On the other hand, the workload and class schedule needs imho to be seriously redistributed as I finished this class with some serious burnout. Maybe I did put more pressure on myself than I should have as the grading of the first homework assignments seemed very strict but towards the second half of the class grading became (very?) lenient on anything I submitted (and you don’t get much personalized feedback…); It was confusing and disorienting what to expect. I also wish they would spread the video lessons more organically over the entire term.
Pros: -Extremely well-done class videos, some of the best I have ever seen -Interesting topics -Contents are very relevant for anything that is going around in the world -Very few (if any) technically complex subjects, you don’t really get stuck in anything
Cons: -Possibly the highest workload of any classes I have yet taken in my OMSA degree. This has truly been a summer of hell for me, the workload was just insane in particular for the first two thirds of the class. There was a week when we had to submit a Quiz (test time: 2 hours, not including prep/review time), a Test (test time: 2 hours, not including prep/review time), and the Individual Project check-in. That was just insane. -If you take this class, you should enjoy writing, because there will be lots and lots and lots of it. -Grading was inconsistent if you ask me: the Homework grading (in particular HW1) was brutal. The personal projects and the Quizzes were incredibly easy (I got 100% in most, even though I made some blatant mistakes). This made it stressful and hard for me to know what to expect really. -Lack of personalized, individual feedback: I guess this shows the limits of online classes open to large numbers of students: the individual attention I felt I got was close to none. For instance, out of a 25-page individual project, I got 4 sentences of feedback from the TAs. -The Peer Review part ended up being extremely tedious and imho time-wasteful. I would have rather have to read 2-3 model, exemplary individual projects, as opposed to having to read dozens of subpar, in-progress projects. I would say that I ended up wasting way too many hours reading contents by peers (that was not teaching me much) where I would have preferred to have more time available to read or watch teacher-sanctioned content.
Tips to pass: -Be ready to work your ass-off the first 3 weeks of the class (where they concentrate all of the mandatory video classes), watch the videos slowly, take notes, organize them. Having these in good shape will make a LOT easier to do the Homework Assignments, Projects, Quizzes and reviewing for the Tests. If you can, block your weekends, even take some time off. -For the 3 Homework assignments, make sure you answer explicitly to every single bit of every single question. I found that using bullet points to make sure I was responding to every subpart of each question was the easiest thing to do. -Remember that you ARE allowed to use ChatGPT / AI for the Quizzes. -Submit the individual project check-ins even if you are (very) behind. They care that you submit something, even if you are weeks behind.
Rating: 4 / 5Difficulty: 3 / 5Workload: 14 hours / week
decuBccQaPEytoOYiWtJ1g==2024-08-11T09:05:41Zsummer 2024
Software Development ProcessGreat course if your are without a background in CS and little work experience . Lectures are good . The worst part is that you don't choose your groupmates ,I got an A ,but ended with a bad group and did most of the app myself ! .It makes an ideal easy summer course or it can be paired with something harder !
Rating: 4 / 5Difficulty: 2 / 5Workload: 10 hours / week
decuBccQaPEytoOYiWtJ1g==2024-08-11T08:55:27Zfall 2023
Database Systems Concepts and DesignI came with little knowledge about relational databases and learned a lot , I think its a very important course for people without a CS- background , the lectures very good and helpful, I had a good group and I got some experience doing a full stack app using spring boot and react , the exams are fair but I ended up blowing the final exam due to some life issues and got a B , but overall great course !
Rating: 5 / 5Difficulty: 4 / 5Workload: 20 hours / week
cRXz1sFin0M1pGkQUS9Ykg==2024-08-11T04:00:01Zspring 2024
Data Analytics in BusinessThis class was easy, and definitely not much of a burden even with the project, but after I completed the course I asked myself why? I really don't understand why this course is required and should at most be an elective for the business analytics students.
As for the material, the first 5 weeks covers regression, which is already covered in other courses. If anything, having a full class in regression be mandatory makes more sense than whatever this course was (I understand there's gripes with the regression course as well but I just think a mandatory regression course in general makes more sense for the program than this class). The professor also simply reads off the slides in the videos so they're really boring and unengaging. The next 3 weeks are in finance and although I found the material interesting and well taught, it was very high level and may make more sense to have an elective course on it that goes in greater detail. Then there was marketing which was just some of the lowest quality lectures you may experience. The videos consist of long tangents about topics such as the history of marketing and making a Facebook Ad campaign. Really not meaningful to an analytics degree. Fortunately, as of this semester, there's no closed book testing on this section so you can get by without watching the videos and just flipping through the notes to answer any questions on them. Lastly there was supply chain which again I thought was well taught but is 1. already taught in mgt 8803 so it's not necessary here, and 2. Is pretty high level and I think may be better off as an elective.
As for the grading in this class, it's really a joke. There are multiple choice self assessments which you can pretty much just flip through your notes to answer the questions without even truly learning. There are 3 homeworks and half of it is pretty much just like the self assessment, and then the other have is coding based which they provide similar examples for. The coding portion is probably the easiest to mess up on in this class but it wasn't difficult as far as a grad degree is concerned. For the midterm, the multiple choice was closed book besides a cheat sheet but it was mainly on regression and some finance so fortunately it didn't involve studying the marketing section, and then the coding was open book multiple choice questions. For the final however, it was completely open book and untimed which was nice for the easy A but I again didn't feel like I was really forcing myself to learn the material.
I didn't find the project to be much of a burden as long as you get a good group at the start of the course, chip away at it throughout the semester, and follow the guidelines. I've heard they don't offer it every semester anymore so this may or may not apply.
I really don't understand why this course is mandatory and what we're supposed to get out of it . Sure it's easier and a lighter course load so you can pair it with something or take it alone if you want a light semester, but is that what a master's degree is about? I've seen others mention that the course being lighter allowed them to really focus on learning R which can be true but again, there are free online courses you can take if you really want to learn that in depth. I really think it would be more worthwhile to eliminate this course and make a different course that's more valuable to this degree mandatory, because I couldn't tell you what I gained from taking this
Rating: 2 / 5Difficulty: 2 / 5Workload: 5 hours / week
4bAmToXB3t9tFYKjhRjd0Q==2024-08-10T23:54:52Zspring 2024
Educational Technology: Conceptual FoundationsI took this class in Spring 2024. The course is split into two phases: an exploratory phase and a project phase. In the exploratory phase, you explore the literature in Edutech and write several papers where you summarize research and respond to prompts in the form of essays. This phase took me around 20 hours per week.
In the project phase, you can choose to either work individually or in a group. There was also an option to do research with Code.org, and this would satisfy the project requirement. You had to apply for this, and from the responses on Ed Discussion, it looks like the majority of applicants got ghosted. It was slightly inconvenient because we didn't have a timeline of when the selected applicants would be chosen, so it was hard to move forward with selecting a project because we were all hoping to be chosen by Code.org, but didn't know when or if that would happen. A couple of people asked about this on Ed Discussion, but got radio silence.
As others have mentioned, the projects are graded very leniently. As long as you turn in your weekly progress checks and make progress every week, you should have no problem getting an A from your mentor.
Rating: 5 / 5Difficulty: 4 / 5Workload: 20 hours / week
4bAmToXB3t9tFYKjhRjd0Q==2024-08-10T23:24:17Zsummer 2024
Mobile and Ubiquitous ComputingThe professor and TAs are not that active on Ed Discussion, but the grading is very lenient. You are able to choose your own groups for the project, so that's nice. They also increased the maximum group size from 4 to 5. If you're not looking to do much work, I recommend a group of 5. It felt like barely any of us did any work but we still got full or almost full points on every assignment. If you're in the HCI specialization or doing OMSCS for the piece of paper at the end, I would recommend this class.
Rating: 3 / 5Difficulty: 2 / 5Workload: 5 hours / week
cRXz1sFin0M1pGkQUS9Ykg==2024-08-10T19:02:51Zfall 2023
Introduction to Analytics ModelingOverall, I thought this was a great course with a great instructor and group of TA's, but I wouldn't call it an easy A by any means.
The lecture videos are some of the best, if not the best, in the program. Joel Sokol is a natural at explaining concepts in his videos in a way that simplifies what may otherwise be complex topics in a way that makes them understandable, which is perfect for an intro/survey course. The TA's in piazza are also incredibly helpful and responsive when it comes to answering questions you throw their way. These were definitely highlights of the course.
The homework difficulty and time commitment is dependent on your experience in R. If you're new to R, you may struggle initially, but it gets better as you get used to it. Worst case scenario, the TA's provide starter code in office hours shortly before the assignments are due which should help you figure out what needs to be done on the assignment to get either a 90 or 100 on them.
What I found to be difficult in this course was the pacing of it. I don't recommend taking it in the summer as the class felt pretty non stop even when I took it in the fall. Every week there's new material being taught, with a homework due, and then you have to complete three peer reviews. This is the same case for exam weeks: new material and homework is released even as you're trying to prepare for the exam which I found kind of rough. As mentioned before, you'll learn a lot and it's well taught, so this is just something to be aware of.
Additionally, the tests themselves aren't the easiest and they make up a large portion of your grade. The exams are purely conceptual and come straight from the lectures, so don't refer to homework to prepare for these. The way some of the questions are worded can get tricky and may require you to think pretty heavily, so my biggest recommendation is to really listen to the videos closely and make sure you really understand everything fully. I was able to do well through this approach but it definitely was time consuming, and I understand why people complain about the exams.
Again it's a great course and you'll learn a ton, but the pacing is fast and the exams require a very strong understanding of the material and having to think through some tricky questions
Rating: 4 / 5Difficulty: 4 / 5Workload: 12 hours / week
cRXz1sFin0M1pGkQUS9Ykg==2024-08-10T18:47:46Zfall 2023
Computing for Data Analysis: Methods and ToolsThe difficulty and time commitment in this class will highly differ depending on your python comfortability going into it. Take the pre-req seriously and go through the CS 1301 class they provide and make sure you're comfortable with all the material there. If you don't, this class will be extremely rough for you. For people who don't drop the course, I recall the exam medians being pretty high so if you feel you're well prepared for this class, I wouldn't get to worried by some of the reviews as the majority of people do well, although I'm definitely a little biased as I use python daily at work.
I found the course itself to be great though! The notebooks they provide are really effective at teaching you the material. The course is essentially divided into three parts: list/set/dictionary/string manipulation, data frame manipulation (pandas and numpy) , and then some modeling concepts. The first module tends to be the most difficult in terms of the difficulty of the questions they ask. The section on floating point precision is definitely the most difficult, along with regex, but otherwise I didn't think the homeworks were too bad. You also don't need to really understand the floating point material conceptually to do well on the exam as it's more just a way to practice string manipulation. For the modeling concepts, although the formulas they derive can get a bit intense, you just have to apply the formulas in python to do well, not actually derive anything yourself. I also love that you don't have to answer every question on the exam to get a full score so that one question doesn't wreck you, and that the exams open internet, just as it would be in a real-world setting. The TA office hours are also fantastic when it comes to helping you with some of the more difficult material as are all the previous exams they provide you to prepare.
I would say the key to this course is coming in with the right amount of python preparation (CS 1301), going to office hours if you find yourself struggling, and doing as many practice exams as you feel necessary in order to prepare for the exam and measure where you're at. Otherwise, it's a great course and I think you'll find yourself learning a lot!
Rating: 5 / 5Difficulty: 3 / 5Workload: 10 hours / week
cRXz1sFin0M1pGkQUS9Ykg==2024-08-10T18:28:46Zsummer 2024
Special Topics: Business Fundamentals for AnalyticsI really didn't think this course was nearly as bad as people say it is (besides the marketing section), although I do question how necessary this class is for the program as a whole. Definitely don't feel like we need two MGT courses.
TLDR: Accounting is great, finance is the hardest, marketing is terrible, and supply chain is well taught and not too hard conceptually but the exam questions can get tricky. Having an exam every few weeks can get exhausting, but doing the supplemental questions is a must to prepare for the kinds of questions you'll see on the tests, and TAs are helpful if there's anything you don't understand. I also felt the bonus points on the exams make getting an A reasonably doable.
The course starts off great as the new professor for accounting, Ryan Blunck, is fantastic. His lecture videos are great and as long as you study the notes from his recorded and office hour videos, you'll be golden for the test. This was the only module where they revealed the class performance and it was overall pretty high. I'm not sure how this module was before he taught it but I would rate this class a 5 if they were all taught by him!
Then there's finance. I thought this section was reasonably well taught but the material was definitely more difficult. The volume of material started to feel a little high by the end and the complexity level of some of the calculation questions are rough. Going to his office hours and going through every kind of practice question covered both in the videos and in the supplemental questions is a must to do well on the test. Once you understand how to answer these questions, I found the exam doable but these questions can definitely get hard, depending on your math and finance background. Go to the TA office hours if you're struggling as the TA for this section, Michael B, was super helpful. You may have to put in a lot of effort to succeed in this section, but I still found I learned a lot.
Next is marketing and I would easily rate this class a 1 if every section was like this one. Yes, it's the easiest conceptually and there's no calculations, but the lecture videos are long and just complete information dumps, throwing definition after definition and concept after concept at you. I found studying for this exam brutal as there was an insane amount of information to memorize. Doing well on this section depends on how easily you can memorize information, and how much time you want to spend memorizing it, only to then forget most of it once you take the exam. I also found the live office hours useless but you still need to go/watch in order to answer a couple exam questions. This section was such a shame as I otherwise really enjoyed the course. I also found the homework difficult to get full points on despite having 10 attempts at the simulation as there's very little guidance.
Lastly, there was supply chain. Bob Myers lecture videos are great and keep you engaged. The concepts covered are pretty straightforward and I didn't find there to be too much information. The calculation problems he covers in his recorded videos aren't bad, but the ones in his live videos get a little more tricky. And then some of the supplemental questions are even more difficult. Go to the TA office hours or ask in ED if any of them are tripping you up. Once you understand every kind of question they may throw your way, the exam isn't bad, but they do get tricky. I also found the homework on this module easier to do well on despite only having 3 attempts. My main gripe with this section is the material is already covered in MGT 6203 so it definitely doesn't need to be covered twice.
Since this was summer I can't speak to business strategy, but overall if it wasn't for marketing I would say this is a good course that is possible to do well in as long as you prepare for the exams by going through and understanding every practice question provided, and capitalize on the bonus points. I also recommend taking this in the summer since they remove a module instead of condensing the material, and you only have 4 exams instead of 5.
Rating: 4 / 5Difficulty: 3 / 5Workload: 10 hours / week
9OnNVU16JiP7Z209SFe05g==2024-08-10T14:16:25Zsummer 2024
Educational Technology: Conceptual FoundationsOverall, I like the idea that you get to work on a project you’re passionate about. There’s no grading inconsistency like some other courses because you’re paired with a TA who acts as your mentor.
However, there’s a chance you might end up with a TA who’s clueless about your project. I was assigned a TA who not only lacked understanding of the project I was building, but also had a condescending attitude. Interestingly, this is the only one out of four courses where I found peer feedback more valuable than the grader’s. That being said, this course could really use some external mentors, kind of like in the Intro to Health Informatics course.
If you’re looking for a reason to tackle an education-related project and aren’t banking too much on the mentor, then go for it.
Rating: 2 / 5Difficulty: 3 / 5Workload: 17 hours / week
eSRcHnT9ALrWaX0GkQIUaA==2024-08-09T22:59:12Zsummer 2024
Machine LearningIt is one of the best courses I ever took (in-person or online). The assignments are open-ended, where you choose the dataset, language, framework, etc. and it's on you to explain why the dataset is interesting for the problem, or why certain hyperparameter variation has a certain impact on the observed performance of the algorithm. Writing the analysis forces you to think and understand concepts better. TAs were super helpful, with FAQs for each assignment and Final Exam. One advice is to start the assignments early. I am a bad procrastinator and started all 3 assignments 4-5 days before the due date. I scored pretty low on the first assignment as I didn't know the rubric, but with the feedback from the first assignment, I could improve my scores on the second and third assignments. Luckily, I got an A in the end. I liked the light-hearted video lessons, where I learnt a lot without being bogged down by a lot of Math.
As I took it in Summer, RL section was not covered by Assignments or exams.
Rating: 4 / 5Difficulty: 4 / 5Workload: 20 hours / week
br09Dorz45zpn9ySp+NC7g==2024-08-09T17:11:44Zsummer 2024
Introduction to Graduate AlgorithmsI completely agree with the other student on the rigid rules that you need master to succeed in this course. What shocked me the most is how the professor managed to turn an algorithm course from quantitative to qualitative.
That's right, the homework/exams and grading of this course is rather qualitative than quantitative. To succeed, writing is more important than coding, saying the obvious is more important than deep thinking. And don't get me wrong, the TAs will not let go any chance to take points off your submission. During the course, you spend way more time to master these skills and to figure out what the professor and TAs actually want, than to learn algorithms.
Avoid this course if you can. If you cannot, bend down you knee and join the gang.
Rating: 2 / 5Difficulty: 2 / 5Workload: 8 hours / week
uEN/KzKVnW2nRAbsEWIbvg==2024-08-09T14:21:06Zsummer 2024
Introduction to Analytics ModelingThis course was challenging for me because I felt that I did not have a strong math background. I did not take linear algebra or discrete math before this class, and felt a bit lost on the formulas and whatnot. When the lecture videos touched on them, my eyes sorta just glazed over them. But they aren't necessarily heavily used in the assignments.
The coursework provided me a good overview of analytical models, what they are, when to use them, etc. The homework assignments were still doable because the TAs did a great job with the office hours in providing guidance. The assignments are peer reviewed. So I would say, if you're struggling, just do your best, attend the office hours, and submit what you can. You can still get a passable grade on the assignments I think.
There were 2 midterms and 1 final which made up a large chunk of your grade. I got mid-70s on the midterms, and mid-80s on the final. Overall, I was able to get a B in this class.
Lastly, I have to say, this course has a very supportive TA team. I've signed up for classes where the TAs can be very condescending and all, and it kills the enjoyment of the course. But this TA crew is A+ and cares about the students and how they progress. Thanks!
Rating: 4 / 5Difficulty: 4 / 5Workload: 18 hours / week
ex/+WEggftoEEjwISXXHLg==2024-08-09T12:16:24Zfall 2023
Graduate Introduction to Operating SystemsSimply a must-take course. I took it hoping it would be a “level up” experience and that’s exactly what it was. It’s hard, it’s a grind, and it doesn’t let up at all, but it’s worth it. You’ll come out of this class feeling like you can conquer any computing problem that’s thrown at you.
Rating: 5 / 5Difficulty: 4 / 5Workload: 20 hours / week
nEwHxTvhoTC0xdgqjqoJZQ==2024-08-09T08:45:49Zsummer 2024
Human-Computer InteractionLikes:
- Mind-opening topics and useful knowledge
- Well organized
- High lecture quality
- Clear grading rubrics
Dislikes:
- Too much readings towards the end of the module (the "Conclusion" module)
- Some readings are just intolerable because they rehash the same idea over and over and over. I don't know why people think Don Norman's book is a classic. While the ideas it convey may be valuable, its writing style is simply horrible.
- Honorlock
- Group project. While I had a good group, the steps we did for the group project seemed very superficial because no one was willing to invest much time in it (we all have jobs and one dude is taking 3 courses per semester)
How to do well in this course:
- Read the rubrics and make sure your work checks all the checkboxes
- At least skim all the readings (especially the readings in the Conclusion modules) because the tests are going to ask you about each of these readings
- Find a group early. In my experience, those who seek to form groups early tend to be reliable and organized. You don't need creative geniuses on the team. You need reliable and organized people. My group picked a super boring topic and went with a super conservative design yet we got 100 because everyone did their work and our work hit all the checkboxes of the rubric.
Overall, I highly recommend this course, especially when you haven't taken a Joyner course. It's well run, and its content is super useful and mind-opening.
Rating: 4 / 5Difficulty: 3 / 5Workload: 9 hours / week
WHiIEH1su6WtKmpqUU6EZw==2024-08-09T04:41:42Zsummer 2024
Data Analytics in BusinessMan, this is WILD! I’m trying to get my kid to sleep and keep up with this online class, but it’s a total circus. That is until I hit the marketing section by Professor Frederic Bien. I start to play the lecture recording, and boom—my kid’s out cold, no fuss, just lights out. I can't express my gratitude enough to Professor Bien for granting me the peace I needed to get an A in this course, THANK YOU PROFESSOR BIEN!!
Rating: 3 / 5Difficulty: 2 / 5Workload: 8 hours / week
ex/+WEggftoEEjwISXXHLg==2024-08-09T03:43:40Zsummer 2024
Introduction to Graduate AlgorithmsTo preface - I have a 4.0 and I got an A in the course. I’m not a slouch that failed and came here to whine. What you’ve heard about the course is true. It SUCKS.
This course doesn’t suck because the material is hard or because the workload is high. Algorithms aren’t that tough to figure out, and analyzing their correctness, runtime, and completeness isn’t hard either. It barely qualifies as math (in the way this course teaches it).
This course sucks because of how Dr. Brito and his gang of pedantic shitstains have designed it. You don’t need to do a ton of work or study an insane amount to keep up, you just need to play their ridiculous game. They’ve created a network of rules and an entire linguistic framework that you need to master if you want to do well. In no way is this a productive or useful way to spend your time if your goal is to master algorithms.
My advice - if you have any interest in the specializations that don’t require GA, just do one of those specializations. If you must take GA, master their game.
Rating: 1 / 5Difficulty: 3 / 5Workload: 15 hours / week
oI2BSEadtql5XmDvBPp4lw==2024-08-09T03:26:35Zsummer 2024
Machine Learning for TradingOverall, it's not a bad class. Not as much programming as classes like AOS and HPC, was less of a challenge than those classes. But still, it's well run
A lot of your grade comes from projects which have super long and specific requirements. The hardest part on the projects isn't the actual dev work, just reading the requirements and getting them all in your head at once (kind of like HPCA in this respect)
My favorite part about this class was the more finance-oriented part. Personally, I had a lot of blind spots going into this class (e.g. didn't know what hedge funds are or what options trading is). It helped cover those blind spots
I would totally be into taking some hypothetical course with Professor Balch that's 100% about finance
Rating: 4 / 5Difficulty: 2 / 5Workload: 10 hours / week
CEIGYVyIqPsZQIhIgvrwmQ==2024-08-08T23:28:42Zsummer 2024
Introduction to Graduate AlgorithmsDr. Gerandy Brito, from Cuba's shores, Teaches Algorithms with tough, rigid cores. Grading metrics, oh so malicious! Leaves us confused, feeling fictitious.
Student welfare? Left in the dust, In his eyes, succeed we must. Grading requests? A slow reply, Leaves us all, wondering why.
Just like a tax officer, strict and stern, Fairness and empathy, hard to discern. Sending students, without cause or clarity, Straight to OSI, with cold severity.
Oh, Dr. Brito, we struggle and strive, In your class, just to survive. Communist roots, with methods so rough, In your algorithms class, the going is tough!
Rating: 1 / 5Difficulty: 5 / 5Workload: 40 hours / week
yPy88wXKhTipgNNP9U+7Sw==2024-08-08T21:00:59Zspring 2024
Privacy for ProfessionalsI enjoyed this class and felt that most of the material was practical for my current corporate management role. The ripped from the headlines articles were easy and interesting to me. I enjoyed the lecture videos and interacting with the professors. The TAs were generally fair and constructive. The weekly workload is fairly low, but I spent about 20 hours studying for each of the exams, including reviewing lectures and building matrices for groups of laws like state breach notification requirements, as this is primarily a privacy law class. Overall I felt it was it was a good class, but worst part if the level of detail you need to learn to do well on the exams.
Rating: 4 / 5Difficulty: 2 / 5Workload: 4 hours / week
sTsralZklUgAsWN86aZZzA==2024-08-08T19:25:23Zsummer 2024
Natural Language ProcessingThis is an overall great course and newly developed. This is great staying away from the hideous old videos for most of the other OMSCS courses.
It's still currently small in numbers and hard to get in, however it will grown soon with more potential TAs graduating every semester.
The material is extensive, very extensive to be honest. In fact too extensive and you barely get tested on anything. Even if about half the videos were taught, you would still get a good education and would not even affect the assignments/exams.
The quizes are mostly hard even though they are usually 2-3 questions, they are tricky so take your time, you only get 2 attempts each.
Midterm and final exams are take home open everything (except for your favourite GPT). These are written questions you need to answer. I found them ok overall but take time to complete.
The 6 homework assignments are the real meat here taking up 70% of the grade. They are all coding with some written question in the final assignment which counts 20% of the overall grade. The first 5 homeworks are autograded on Gradescope but some take quite a while to run.
My suggestion is to use Google Colab with paid subscription, this is the most efficient way of working through these since the starter code is adapted for this platform in mind. You can do it on your environment as well and works ok for some assignments but a pain to adapt for others.
The homework assignments do build up, from only a few lines in the first assignment to quite a sizeable amount of lines in the last one. Be prepared for this. Another tip is to start early HW5 (the last one). I left mine at the last minute along with the exam since the deadline was on Wednesday instead of the usual Sunday. I rushed through the final assignment and the exam merely missing an A by a hairline!
Final note on the videos, the first half of the course with the videos made by the professor are absolutely the best. The second half where Meta AI folks do the videos are absolutely horrible, dry and for most part pointless. I would not suggest you skip them but prepare to be bored to death. I really hope these are done again in the future as this is one of the best classes in OMSCS.
Rating: 4 / 5Difficulty: 3 / 5Workload: 12 hours / week
OURQBV6RW/nYmubrd+kEYw==2024-08-08T04:32:04Zsummer 2024
Statistical Modeling and Regression AnalysisI am disappointed that GT continues to endorse a professor who does not appear to enjoy teaching. All quotes are from public Piazza posts by either Dr. Serban or her TAs.
“… you will get 0 points, and there will be no exceptions.”
“we will not provide samples or any additional support.”
“What I would also suggest is that we’ve had within some Piazza threads a surfeit of external and deficit of internal blame attribution (for those of you interested in psychology) in pondering the open-ended question if I did not perform as well as I liked on the Midterm, what is the cause?”
“Yes, it may be stressful to perform under time limits, but isn't this what you should expect from your job also? Don't you have timelines, deadlines and work cramped within short times to deliver for your job?”
“There are several reasons we use timed exams. One is that the instructor team can review the honorlock videos. There are many situations when we need to do so. We have limited amount of time to review many hours of videos for several students.”
“Third, a timed exam within a reasonable time doesn't drag the exam for hours. I don't think you (or at least some of you!) would want to spend hours at end for taking exams. I am sure you have better things to do than siting 4 hours to take an exam.”
” every single TA takes the exam before it's released. I personally took it and it took me 75 minutes to complete and was very simple.”
“If you want an apology from me personally for something, drop me a private post and I’ll probably extend you one (the probability is high). If you feel you need to apologize … I would suggest doing that, also privately. “
“We review every Honorlock video in detail and can see exactly everything you do and say during the exam as well as everything that happens on your screen during the exam. We can verify every comment you say to us and check if it's correct or not very accurately using Honorlock.”
“We (the instructor team) have been informed that we will need to submit such cases to the Office of Student Integrity, which will follow up with various warnings or disciplinary actions.”
Rating: 2 / 5Difficulty: 4 / 5Workload: 12 hours / week
4r+C/SIbKMb8nRfLk1VlQg==2024-08-07T20:00:56Zsummer 2024
Reinforcement Learning and Decision MakingTLDR; This class is very cool. Project 3 is very hard. If you're interested in the material, you should for sure take it.
This is a really cool course. The lectures are very similar quality to ML, so if you liked ML and the corny way Prof. Littman and Isbell lecture you will like this. I found the lectures to be fine. They cover the material, but there is a lack of depth that you are expected to extrapolate from the assigned readings. The other thing to note is that the lectures do not really cover material that is necessary for Project 2 and 3. Project 1 was really easy after the horror that was the 10-page papers from ML. If you want to do well read the paper literally everything you need is in that paper. Project 2 was also pretty easy. I don't recall if they gave the best advice about papers to read and reference for that, but look up DQN, DDPG, PPO, or REINFORCE and you should be able to figure it out. Project 3 was very difficult. This project covered MARL in a simplified Overcooked environment. And a big part of why this assignment is hard is because the environment is implemented in python and slow. Start this one as soon as it drops. Clear your calendar for the three weeks the assignment is available. I did not solve all of the layouts, but the penalty for not doing so is "small" (they don't specify what it is exactly). The HW is all very easy as well. I would recommend getting them out of the way quickly so that you can spend all your energy on the papers. The Final exam for this class is pretty annoying. There was no provided study guide and no clear communication of what would be on it. But don't worry because as long as you're ahead of the average you should be in good shape. Honestly, feels kinda disingenuous to have a final exam that is multi-choice/ multi-answer for a class that almost entirely focuses on analysis rather than definitions and rote memorization, but I also understand that grading a free response exam would take a lot of time. I got right around 50% and still got an A, so unless you really screwed up your projects or missed a homework I wouldn't sweat it too much.
I'm giving this course a 5 on quality because it was awesome, but recognize this is a graduate level course where most of the learning is done outside of lecture. TAs as always are going to give really vague and unhelpful answers to questions that get to the heart of assignments. I'm giving a 4 on difficulty because I'd say that is the average difficulty level, but Project 3 is seriously no joke and has been the hardest assignment I've worked on at OMS so far.
Rating: 5 / 5Difficulty: 4 / 5Workload: 15 hours / week
ZduXZVe+NcBIRDua2dy92A==2024-08-07T19:54:57Zsummer 2024
Machine Learning for TradingThis was my 3rd course in the program (after AI4R that I loved and KBAI that I hated). The course was a mixed bag for me but overall I'm not regretting taking it (unlike KBAI that has no right to exist). I work in finance and hence was not looking to learn much on the "trading" side of the course (which I think was a bit rudimentary). My goal was to have an intro to ML. With that, I think this course suited my goals, albeit requiring more work than was necessary for that. I think I gained some basic intuition on the supervised learning (regressions, decision/random trees, ensemble methods) and reinforcement learning (Q learning with or w/o dyna). On top of that, I learned how to use Python (scipy) optimizer and (my favorite) how to vectorize in pandas and numpy - that really speeds things up while making the code neat at the same time.
On the negative side:- Excessive amount of additional reading, most of which is useless (e.g. Handbook of AI and Big Data Applications in Investments - the book describes some use cases but it really does not teach you anything. Yet, the chapters are quite lengthy and takes a lot of time to read)
- Projects are quite intense, especially for the Summer. Project 6 (on technical indicators) took me long time despite having a lower weight. Obviously projects 3 and especially 8 were very time consuming, it was unreasonable to give only 1 week to complete them.
- Exams - it was not clear at all what these exams were supposed to assess. Very strange questions formulated using un-human English.
- Not a deal breaker, but some of the lectures are of a very poor quality. I really struggled to hear or see something on some of the videos.
Rating: 4 / 5Difficulty: 3 / 5Workload: 17 hours / week
t7u0m0cOWvHAoSzQgpzUqw==2024-08-07T19:42:32Zsummer 2024
Network Science: Methods and ApplicationsThis is the worst course I have ever taken. The instructor and TAs are unprofessional, and the course is not useful at all. They changed many scores without any reason and gave the worst scores. No one responds to your questions if you send an email. Everything about this course was bad. Do not waste your time.
Rating: 1 / 5Difficulty: 3 / 5Workload: 10 hours / week
kydpWvukGC+Dp9OviDFR2Q==2024-08-07T19:17:46Zsummer 2024
Software Architecture and DesignBackground: This is my 7th course in the program. I currently work as a developer.
This is the worst run course (and least favorite) I've experienced in graduate school (this will be my 3rd graduate degree/program). I'll give an overview and try to be specific to help future students. I have also shared my thoughts directly with the instructors, so hopefully they make some changes.
First of all, I do genuinely think the professor and instructor are interested in students actually learning. I think they mean well, but when it comes time to prepare and do the work, they just don't. The big problems can really be summed up by saying that their ability to communicate expectations just doesn't exist. The assignment documents that are supposed to declare the requirements are unclear and incomplete. They think they can make up for that by making Ed Discussions and office hours required. This means they can throw out random tidbits of information on those platforms and expect those tidbits to become assignment requirements. In theory, this would be confusing and disorganized, but manageable. I found myself (and my group) reading posts over and over and going back through office hours videos over and over to make sure we didn't miss a change to the assignment requirements. The real problem is that the TA's have different opinions about the requirements. So, you have one TA answer one way on Ed Discussions, then another TA answer another way on office hours, then your grading TA never even read or listened to any of that and have a completely different opinion. For example, our very first group assignment, the first comment (and a few others) for point deductions was a direct contradiction to a written response on Ed Discussions. We pointed this out and were awarded points back - we shouldn't have to do that. Bottom line: ask very detailed questions on Ed Discussions so that it's in writing, and don't let them get away with vague answers. Instructors, if you're reading this, please please please make your assignment requirements crystal clear.
They also changed the overall big project assignment from previous semesters, and they were too lazy to fully update the document, so there was a lot of residual, irrelevant, and confusing information.
The "exams" are quite odd and highly subjective. "Which of these things is the best and worst and why?" Your score is very dependent on who grades it and what mood they are in. Some items it seemed like they didn't even read it and just gave a good grade, while other items you'd think "how on earth is that wrong and why THAT?"
The course lectures are super old and boring. And there are a million of them. And you have to watch them and take regurgitation quizzes. No one involved in the course today had anything to do with those videos at any point in their lifetime.
Finally, grading. It's slow. And as previously mentioned, it's inconsistent and seemingly random. We never did get a grade or any feedback from the big final project (ya know, the one we spent over 100 man hours on). That's a let down and completely unacceptable in my opinion. It feels like they just never even graded it and gave out final grades. We never got our "participation" grades or feedback from our group members. I ended up with an A, but I feel gross about it. Of course I worked hard, but I feel like they just handed out grades randomly.
I'm glad it's over. I'm upset I wasted my time. As someone who has been an educator my entire adult life (I'm almost 40), I'm disappointed in Georgia Tech for letting this happen.
Rating: 2 / 5Difficulty: 3 / 5Workload: 20 hours / week
loGoLaRAAghsaOjF7dJHsg==2024-08-07T17:07:41Zsummer 2024
Machine Learning for TradingI'm not sure how or why ML4T exists in this program. It feels like an intro level undergrad course. The only reason you should take ML4T is if you want easy credits or have never taken a programming class before.
The course content is very outdated and extremely easy if you're decent at coding. Additionally, the Joyner style reports hold your hand to such a degree it's actually difficult to lose points as long as you're referencing the rubric when you write them.
Avoid this class if your goal is to learn. You'll gain more knowledge and achieve better trading results by watching a few YouTube tutorials that cover modern trading methods.
Rating: 1 / 5Difficulty: 1 / 5Workload: 5 hours / week
ZduXZVe+NcBIRDua2dy92A==2024-08-07T14:36:20Zspring 2024
Knowledge-Based AITLDR: Do NOT take this course - you will spend an incredible amount of time but learn next to nothing out of it.
This has been an absolutely worst class for me, not only in the program but probably among the other 20+ graduate courses I've taken throughout my life. There was maybe a course or two where I learned less but then they required much less work. There were courses with higher workload, but then I learned much more from them. This course feels like an incredible amount of busy but absolutely useless work. Even the lectures: very lengthy but with minimal breadth. A student in our class summarized the content of the entire course in a 19 page PDF and that was all in it. And even this summarized material has very little to no practical use. Some mini-projects and the course-long project were kind of fun to work on but they had nothing to do with the course material. Also, keep in mind that each programming assignment should be accompanied with a write-up. On top of that, there are 3 homeworks that are purely write-ups. So it feels that you are taking an English class, not a CS one. To complete the picture, the questions on the exams were also worded in an incomprehensible way. The only good thing in this course for me was that working on the course-long project I got an intro to OpenCV that I wanted to learn. Finally, if you care about your grade (that I was not much though somehow managed to get an A), the written parts are graded absolutely arbitrarily and no re-grade request were acknowledged.
Rating: 1 / 5Difficulty: 4 / 5Workload: 40 hours / week
ZduXZVe+NcBIRDua2dy92A==2024-08-07T13:40:56Zfall 2023
Artificial Intelligence Techniques for RoboticsThis was my first class in the program and I've really-really enjoyed it. I learned/recalled many interesting concepts (Kalman filter, particle filter, PID, SLAM, Search Algo's), the projects were interesting and well organized and the workload was reasonable. I wish all the OMCSC courses were like this one. After taking 3 courses in the program (the other two were KBAI and ML4T) I can tell that this one has yet been the best from the amount I learned vs. time and effort spent ratio.
There are plenty or rigorous reviews here already, so let me just say two things:
- Do yourself a favor and take this class - you will likely learn a lot without being overwhelmed.
- The discussion board has been very helpful. Not once I was stuck on a project and sure enough, somebody was facing the same problem and already discussed it on Ed discussion.
Rating: 5 / 5Difficulty: 4 / 5Workload: 12 hours / week
S4qbEenZksl0HwqhSf0WrQ==2024-08-06T21:22:10Zsummer 2024
Artificial IntelligenceGood and fun class, but when people say its hard, they are not lying. The class is 6 projects, a midterm, and a final, with optional homework assignments along the way. I ended up getting an A in the class but I spent a lot of time on it. I took the class in the summer - I DO NOT RECCOMEND THIS. The accelerated schedule meant that the TAs had to cram things together so much that we had two projects due on the same weekend!
A lot of people are scared of this class because they think it will require a lot of math. You will probably need to have taken both a Calc 2 and Linear Algebra class to understand some of the concepts here, but that doesnt mean you'll actually need to be DOING any calculus or linear algebra. Most of the actual calculations will be done by python code that you write, but you just need to understand the concepts so that you know what code to write.
Each project will take approx 20-30 hours of work, and the lectures are not sufficient to teach you the concepts you need to complete the project. You are expected to do additional research as you work through each project to learn the material. The projects themselves are fun, and if you're new to things like ML, Data Science, Neural Networks, etc., then these projects will do a great job de-mystifying them for you.
The midterm and the final are both fully open notes, open book, no time limit. But they are HARD. They give you one week to complete the midterm and final and you can expect that they will take you at least 3 days each. In many cases, there is material on the exams that is not really covered in the lecture material, and you'll need to do some new research to find the answers. In some cases, they will link you to the material you're expected to use, but in other cases they won't. (This is DESPITE the warning that you're not allowed to use outside materials to help you on the exams!)
For example: the lectures for Module 5 teach you the basics of Bayesian probability, and show that you'll need to do some basic multiplication. Then, for module 5 on the midterm, you're expected to calculate the probability density of the normal distribution (aka the gaussian integral). That wasnt covered at all in the material!
There are also optional homework assignments along the way. These are small 1-page assignments with a few sample questions on them for you to answer. They were only worth 1% of the grade so I skipped them completely, but the teaching staff mentioned that they were considering increasing their weight for future classes.
The TAs were generally responsive to questions and problems as you had them, and they were gracious enough to give the students the opportunity to argue with the TAs about the correct answers on the exams, in case the question was misleading or could be interpreted differently. Many classes would simply tell you "no, you got it wrong and thats final", so I appreciate this from them.
Overall, it was a really fun class that I learned a lot, but I definitely had to work for it and I'm really glad its over.
Rating: 4 / 5Difficulty: 4 / 5Workload: 20 hours / week
PDq7LArpArj14f2A8Zf8QA==2024-08-06T21:11:38Zsummer 2024
Data Analytics and SecurityThis course was one of the worst I've ever taken. The content was extremely disappointing, and I felt like I learned nothing. The videos provided were unrelated to the assignments and projects, making it difficult to follow the course material. Also, the quality of the videos was poor, with unclear audio.
The assignments were very easy yet ambiguous, often leaving me confused about what was expected. The quizzes are very easy and everyone get high grades in them, but, this led to an unfair grading system where the TAs and professor would randomly deduct points without clear reasons, just to create a grade distribution. So, even though the course is very easy, don't expect to get a good grade. There's a high chance you might end up with a lower grade than anticipated due to the arbitrary grading practices.
Additionally, the feedback on assignments and projects is often given so late that there's no time to apply it to future work. By the time we receive feedback, we've already turned in the next few assignments, making it impossible to make improvements. This delay gives them the chance to deduct points for the same issues repeatedly without giving you a chance to address them.
Rating: 1 / 5Difficulty: 1 / 5Workload: 10 hours / week
MlkY7VItW3Qr7X0GIHYrLw==2024-08-06T20:06:03Zsummer 2024
Introduction to Information SecurityThis class now consists of 9 projects rather than the 4-6 that the previous semester reviews mention. The course instructors in their infinite wisdom decided to implement this change for the first time during a shortened summer semester.
Pros:
- All assignments are auto-graded so you have immediate feedback.
Cons:
- Almost every assignment uses a different language/toolset. If you aren't familiar with the language/toolset required for the assignment, you end up spending most of your time dealing with syntax and implementation issues even if you understand the underlying concept behind the assignment.
- Assignments rarely built on one another and were more of a scattered survey of the course topic. Combined with only having one week per assignment, I forgot anything I learned from the previous assignment context switching to the next.
- TAs are more focused on preventing students from helping each other too much rather than actually answering questions in a helpful manner.
- It doesn't seem the professor cares too much about the class since he rarely communicated in Ed. The professor and head TAs also hosted "joint" office hours, combining students from different classes, which caused a lot of confusion and miscommunication.
I stopped working on the final assignment as soon as I completed enough problems to secure an A in the class. It seems like most students did as well, given that the average for most assignments was an A but an F for the final assignment. This was the worst class I've taken (out of 9) in the OMSCS program and the class where I learned the least.
Rating: 1 / 5Difficulty: 3 / 5Workload: 20 hours / week
1f2dHYbVWqNiGsZDLA7qVg==2024-08-06T18:28:59Zsummer 2024
Machine Learning for TradingBackground: Engineering undergrad with no formal CS background and not much Python experience. 2nd class in OMSCS. This class really taught me how to use Python for some pretty cool projects and is a great introduction to Machine Learning. Some of the projects are more difficult than others and are weighted appropriately. The concepts taught are very interested, especially those are actively invest and you learn a lot of important information from the financial side.
I recommend trying to get ahead as all of the projects and lectures are posted, especially if you have travel plans as during the summer semester there was a project due every Sunday.
Rating: 5 / 5Difficulty: 3 / 5Workload: 10 hours / week
flJaE09EClG3Ga01HdnWAg==2024-08-06T17:44:09Zsummer 2024
Database Systems Concepts and DesignAll the material learned throughout this course was actually very interesting and enjoyable. The staff and logistics made this course a strong demotivator. To begin, no matter what is going on in your life, there will be NO accommodations. As explained in the prior review, the class average on the final exam was a D. You would think they would curve because the most important exam that makes it or breaks it for students had a D AVERAGE. That should be a strong indicator that there is something seriously wrong with the way this class is structured. The was no regard for student's life outside of school, shit happens.
TLDR: Grading is extremely harsh and strict (class average of D on final exam with no curve). No accommodations no matter what is going on in your life. Little to no regard for student life outside of school. Material was very interesting.
Rating: 3 / 5Difficulty: 5 / 5Workload: 15 hours / week
wkGYdmqv0ccvTC91ySqHnQ==2024-08-06T03:12:41Zsummer 2024
Machine LearningThis is the third course I'm in. Personally, I thought this course would involve some algorithm understanding through implementation, but it is purely focused on analysis, and students are free to use whatever package they want.
As I did this over the summer, there were only 3 assignments and 1 final exam. Scores: A1 (Supervised Learning)-94, A2 (Random Optimization)-78, A3 (Unsupervised Learning and Dimensionality Reduction)-90, Finals (Questions were a consolidation of 15 topics): 81, Overall: 88. In this term we had an additional, hypothesis quiz and a reading/writing quiz.
While I did learn more about the algorithms and whatnot, my biggest complaint is that while the assignments are unbounded (open-ended), at the end of the day, you are being graded against a hidden rubric.
Personally, I don't like how the lectures are being conducted where two professors bantered, I find it distracting at times (although I did laugh at "you can't ride a cougar"). In terms of lecture videos, I would prefer something like ML4T where stuffs are delivered straight to the point in the lecture videos.
In regards to the infamous "TA roulette", I felt that its more towards whether he/she put in the necessary effort to read your paper. As you can see from my A2 grade, I got quite a lot of marks shaved off despite putting in the same effort and doing proper analysis in all 3 assignments. One of the comments I got was I did not list how I could improve the algorithms, which I did. I raised this up in Ed to find out what the deduction was for, but it was ignored despite my post being viewed by multiple different TAs.
In terms of assignment feedback, some of the comments were pretty useful, but don't expect high-quality comments. Most were limited to the current assignment, and you can't really "bring it forward" to the next assignment. I have classmates who received comments such as "Overall good report, keep up the good work" and ended up with a 54. I myself received comments such as "While I like your ...., I would prefer .....", which indicates that even if your report is rock solid/interesting as long as it didn't hit the hidden rubrics, then you will have marks deducted.
As for the exam, I didn't really prepare for it and just skimmed through the video twice while watching TikTok videos. The format is in MCMA and had a 3 hours time limit, but I only spent 40 mins to complete it. Truth be told, I'm so damn demoralized after A2, and I just want it to be over.
All in all, you should take this course if you enjoy open-ended exploration and you do not have any writing experience (e.g. publishing conference/journal papers). Otherwise, I think you can give this a miss.
Rating: 2 / 5Difficulty: 3 / 5Workload: 30 hours / week
HTfiMV4RG5iSnsJSdVqlwg==2024-08-06T00:27:39Zsummer 2024
Special Topics: Data Analysis for Continuous ImprovementI really enjoyed this class. It was sort of a chill class with a lot of tools and techniques to operate lean. This is the type of class you combine with a demanding class. The complementary green belt certification is also a nice one.
Rating: 5 / 5Difficulty: 2 / 5Workload: 4 hours / week
6A1Io09FMJHbI1E2CSSREQ==2024-08-05T19:41:42Zsummer 2024
Machine LearningThere was definitely some value in taking this course, but in general I found that it didn't respect students' time and was littered with needless ambiguity.
A lot of your time in this course will be spent on superficial things related to the project papers, like wrestling with LaTeX formatting and tweaking plots to abide by the page limits.
The project FAQs are helpful, but far from comprehensive. I understand that the ethos of this course is to keep the projects open-ended to promote curiosity and exploration, but this value proposition falls apart when you realize that TAs spend 5 minutes max skimming your paper and your in-depth and organic analysis will likely fall on deaf ears. This isn't meant to be a knock on TAs, I think this is mainly a symptom of this course design being a suboptimal fit for a high-scale online program like the OMSCS.
The lectures with Isbell and Littman are top-notch. The comedic banter was maybe slightly overdone, but in general I found that it made the content more engaging and helped me absorb it - definitely a strong point of the course. The final exam (45 MCMA questions) felt very fair and comprehensive.
Advice for those taking this class in the future: skip the office hours (there may be some sparse insights sprinkled into these, but I attended 30mins of one and found it to be a useless cycle of students repeatedly asking the same questions and TAs giving vague and unhelpful responses), use GPT shamelessly for the reports (this is within class policy), and be very liberal with bolding/italicizing key concepts in your report (I noticed a spike in my project scores when doing this, which suggests even further that TAs hardly give your reports the time of day).
Advice to class administrators would be the following. Remove much of the focus on Randomized Optimization, and instead give this time back to expand the Supervised Learning part of the course which IMO is currently a bit neglected. Tuning a neural net via a genetic algorithm is a fun little thought exercise, but making it a focal point of a major project is esoteric at best and pointless at worst. Looking back, this unit/project seems like it was over-exaggerated just so that Isbell could shill his MIMIC algorithm, which cheapened the experience. Also, providing exemplary papers at the beginning of the class (from past students, not a random MICCAI showcase paper that has little relevance to assigned projects) would be immensely valuable in removing ambiguity in the assigned work.
Overall, this is a mediocre offering for the flagship course of the ML OMSCS specialization. Tightening up the screws on (1) making project requirements more transparent and (2) shifting content focus towards the more applicable/relevant material would go a long way to making this a more rewarding student experience.
Scores: A1 (Supervised Learning): 73/100 A2 (Randomized Optimization): 74/100 A3 (Unsupervised Learning): 87/100 Final Exam: 40/45 (89%)
Final grade (pre-curve): 85.5% [A]
Rating: 2 / 5Difficulty: 3 / 5Workload: 15 hours / week
exenhSmf5lOOcSoZUrQd+Q==2024-08-05T13:58:40Zsummer 2024
Software Development ProcessAs someone who does not work in software engineering, it was a useful course. My main take away was learning how to use git properly and code integration in a team setting. Some of the cons of the course was that you aren't able to select your team - I appreciate the staff grouping people in the same time zone together but some of my team preferred to work on things last minute while others preferred to work on things early which made it challenging. I also had a personal situation come up during the group project and there are no special considerations for the group project. I didn't know my team and didn't want to be a burden so contributed as best as I could which made dealing with my personal situation challenging. I can see how anyone working in software engineering may not find this course particularly stimulating, a comment from someone working in the industry was that their experience is that things work very differently in the workplace now. Overall I finished the course with a high A so I would not rate this very high in terms of difficulty and would recommend this course for anyone who is not a software developer.
Rating: 4 / 5Difficulty: 3 / 5Workload: 8 hours / week
qATGWtZ1g4E/4np6V1uifg==2024-08-05T12:28:14Zsummer 2024
Artificial IntelligenceOther reviews have covered the mechanics of the course so I’ll skip that, except to say that this semester, despite being summer, nothing was cut out. All 6 assignments, midterm, final, challenge questions. I spent around 12 hours a week on average and got an A. Some weeks were 30 hours and some were 0.
Overall, this was a fun class and a good intro to AI. The projects are fantastic, especially the ML project where you implement decision trees and RFs from scratch. You need to be pretty good at Python and at algorithms to succeed in this course, but if you’re good at those this is a fun class with a lot of learnings.
Rating: 4 / 5Difficulty: 4 / 5Workload: 12 hours / week
ZpFEvoC7OsbW7Ex20fUBcw==2024-08-05T07:30:25Zsummer 2024
SimulationI initially didn't want to take this course mainly because I used SimPy in 6501 instead of Arena because of how many complaints other student had with Arena. However, the reviews for PM and DO weren't as great at this course and a friend convinced me that it's really not a lot of Arena. She also repeated over and over how funny Dr Dave Goldsman is. I'm sure that in my long career as a student, I haven't met a funnier professor... he would have excelled as a profession comedian if he would have pursued that path! I was surprised at the immense amount of material covered in this class. And, as usual at Georgia Tech, they found an infinite number of ways to generate problems on the material. If you have time, I recommend going over the bootcamp course... It should help you with the first exam, at least. Watching the posted exam review questions were really helpful as well... Although I caught on to those late. I felt comfortable doing the HW problems by myself but, a word of caution, they can bring a set of totally different questions on the exams. Make good cheat sheets and practice as much as you can. It was one of the toughest courses I've taken during the summer but fun nevertheless.
Rating: 5 / 5Difficulty: 4 / 5Workload: 18 hours / week
CEIGYVyIqPsZQIhIgvrwmQ==2024-08-05T04:54:35Zsummer 2024
Statistical Modeling and Regression AnalysisI think after all the exams and everything, I remembered the name of the Head TA - Olaoluwa Dami Alebiosu, better than any of the course content this summer.
Will try to be as neutral as I can. The rest of the reviewers will probably express themselves better.
Rating: 3 / 5Difficulty: 3 / 5Workload: 30 hours / week
Y0p+1lfk2jRxT+Y8MpA7lA==2024-08-05T02:51:31Zsummer 2024
Machine LearningI entered the course with a lot of apprehension given mixed reviews but I ended up really liking the course. It is a proper graduate level course which treats you like an adult and lets you decide how much you want to get out of this course.
To get the most out of this course it is strongly recommended to have basic understanding of ML algorithms and SKLearn package coming in. Main learning is through the assignments, each of which requiring anywhere between 40-80 hours to complete. If you chose a bigger or a more complex dataset then the time required could increase. Therefore, as soon as the assignment is released, you should be all over it. The Head TA would post an immensely beneficial FAQ about the released assignment which would provide extremely helpful insights which for me was the highlight of this course. There is no rubric provided for the assignments and therefore the FAQs for all practical purposes is the rubric. The office hours are extremely helpful and attending(or watching recordings of the OH) is highly recommended.
In the summer term we had 3 assignments worth 20% each (reinforcement learning section and the related assignment was not covered in the summer term), final exam worth 30%, a trivial research paper reading/writing quiz worth 5% and a hypothesis quiz pertaining to another research paper worth 5%. It was mandatory to submit assignments in Latex which was a bit annoying but chat gpt should come in really handy here as it can provide you with the sample codes for writing equations, stacking images etc. The biggest challenge in the assignment (esp. with larger datasets) is going to be the training time. You will have to do multiple iterations of training as part of your hyperparameter tunings and experimentations and then present all the findings with a cogent analysis within 8 pages. I can't stress enough....START EARLY and chose reasonably sized datasets. I had chosen the publicly available Taiwanese credit card default dataset (c. 30k rows, 20 columns) and bank marketing dataset (c 48k rows, 16 columns) and these turned out fine. I ended up getting 98%, 75% and 85% on the assignments but I really put in a lot of effort in my papers.
The lecture videos were the least appealing aspect of this course for me but still I went through more than half of the lectures (I simply couldn't do more). I would usually pick up a concept that I am struggling with (e.g. PAC Learning) and try to gain a better understanding about it through other sources. The TAs provide a problem set a week before the final exam opens. Although submitting the problem set is options, it is very highly recommended to spend time on it. I would have been butchered in the final exam if not for the problem set (ended up getting 35/45).
All in all, a challenging yet rewarding course. Special thanks to the the lead instructor TJ and head TA Dan for providing the necessary help and mentorship.
Rating: 4 / 5Difficulty: 4 / 5Workload: 15 hours / week
w8O28GZbi4QsOKRokvPQ3w==2024-08-05T00:25:53Zsummer 2024
AI, Ethics, and SocietyThis class is as easy as everyone says it is, so it's good if you're pairing with another course or need something easy because life is otherwise busy.
Here's the rub, though - it actually has something important to say, and I would recommend that anyone in the ML specialty take this course. Specifically, this course teaches one thing: the bias/variance tradeoff can have dire, real-world consequences, and you need to think about how to navigate this tradeoff, or change your models so that you modify how bias is shaped.
In a vacuum, this seems like a really straightforward statement, but when you're inundated with tons of bad statistics, bad applications of data, bad applications of models, and bad modeling in general, it makes you reconsider your approach.
Similar to how ML4T is not really about trading, this class isn't really about ethics, but rather how ML models can be difficult to wrangle even in the best of circumstances, and being a good technologist/data scientist means understanding the nuances of the real-world implications of your models.
So, yeah, it's easy, but the core of the idea of the course is really important, and lesson really only becomes clear after you've been beaten over the head with it a number of times.
Rating: 4 / 5Difficulty: 1 / 5Workload: 9 hours / week
hZ8cIAXOZAXnAf1ZLWkMmA==2024-08-05T00:21:05Zsummer 2024
Introduction to Graduate AlgorithmsThis course requires significant improvements, starting with a thorough reassessment of the role of teaching assistants (TAs). I am extremely dissatisfied with the current level of involvement of TAs, who seem to be running nearly the entire course, from setting assignments to crafting exam questions. It is evident that these TAs do not possess the qualifications of a competent professor, and their dominant role in the course is inappropriate. TAs should assist professors, not replace them in core teaching duties.
Furthermore, if the professor is already aware of this setup and permits it, then it must be stated that their performance does not meet the standards expected at Georgia Tech. In a course where the stakes of assignments and exams are so high, the presence of significant ambiguities in the coursework and exam questions represents a serious educational failure. The professor needs to take a more active role in overseeing and reviewing the coursework and exam designs to rectify these issues and ensure that the educational quality of the course is restored to an acceptable level.
Rating: 1 / 5Difficulty: 5 / 5Workload: 30 hours / week
plbmwzcIJX9pB7AD3WS20w==2024-08-05T00:20:10Zsummer 2024
Introduction to Graduate AlgorithmsI recommend not to take this course because exam have 66 percent questions subjective and TA are free to cut marks as much as they can. You can get 20 of 20 or 1 out of 20 even if you are correct. It all depends on how TA interpret it. Regrade request depends on whims and attitude of TA, If same TA reads regrade request, It is useless to get makrs in regrade request. MCQ questions and quizes have negative marking and form 33 percent in exam. Quizes are only 10 - 14 percent. Coding home work are restricted to special constraint of classes and uses, coding outside rules lands you to heavy peanlity.
I recommend to go for Interactive Intelligence specialization to avoid this course. I got C grade in first attempt, and B grade in second attempt. I should get B grade in first attempt and A grade in second attempt. Course is getting tougher with time due to unpredictable grading, and introduction of new rules in homeworks. you can know it from https://lite.gatech.edu grade distribution in all years. last summer as per chart 11 percent student dropped the course and 20 percent student got grade below B which is needed as minimum for graduation in all specialization except interactive intelligence. Over all trend was in 2019 (dropped + grade below B) were 20 percent. Now it is 30 percent.
Rating: 1 / 5Difficulty: 5 / 5Workload: 15 hours / week
/oDJdM7IhrjkqvNwn2umTA==2024-08-04T22:31:30Zsummer 2024
Introduction to Information SecurityI found this class easy overall and I’m someone who does not know much about security. Took it over the summer so it was fast paced with 8 or 9 projects each due every week. No exams and you are mostly doing capture the flag type hash submissions. TA gets some flack, but they were fair.
Rating: 5 / 5Difficulty: 2 / 5Workload: 6 hours / week
Mg8VbB4wqnj8Dv27PhxvHQ==2024-08-04T22:01:04Zsummer 2024
Software Development ProcessMy background: I graduated with a bachelor's degree in CS last summer, and started working as a Java developer earlier this year. This was my second course in the OMSCS program, and I got an A with a 95%. The only assignment where I didn't score above 90 was Assignment 6. It's a tricky one, so watch out.
Overall, this course is fine, not bad at all. It provides structure and teaches foundational aspects of the software development, including version control, testing, and the software development lifecycle. If you haven't learned these before, you'll find the course very useful. However, if you've taken similar courses or have been a professional developer, you might find it too easy and potentially disappointing.
The course is graded based on 6 different assignments, 1 group project, and 1 individual project. The first five assignments are very easy and straightforward, if you have any Java and Git experience, you can probably complete each one within 1 or 2 hours. To be honest, I almost forgot I was taking the course during this time because the assignments were so easy.
After the first five assignments, we started the group project. In this course, the TAs randomly select your teammates, which I think no other course does. This can lead to very different experiences depending on your group. I've read some horror stories here and on Reddit, so I was a bit nervous. However, I had a great team. Everyone was great, punctual, and professional. We almost got 100% on our group project grade, so my experience was excellent. The group project itself wasn’t hard, but it was busy because of the group work. It required some time for meetings, and there was also peer pressure to perform well.
Then there's Assignment 6, which like everyone said has the biggest potential to lower your grade. I believe the average score for this assignment in the previous semester was in the 60s, and for us, it was in the 70s. In comparison, the average score for almost every other assignment (including group projects and individual project) in the class is above 90%. So, there's a significant drop. I wouldn’t say it’s difficult, but it is very tricky, and you need to answer questions in great detail. The TAs will only give you half credit if your explanations aren't clear, which happened to me. This is the only assignment where I didnt scored above 90%, for all the other assignments, I scored 95% and above.
As for the individual assignment, I found it to be alright. It basically requires you to write code using TDD. So if you haven't worked with TDD before, this could be a good learning opportunity.
So in the end, i'd say if you're unfamiliar with Git, testing, Java, or OOP, you might actually struggle with this course, but learning a lot. On the other hand, if you have experience working as a professional software developer, you'll likely find the course too easy. So I think this course is ideal for someone who knows just Java and OOP but has no experience with version control, testing, or working in a team. If that describes you, you'll find this course very useful.
However, personally, I think this course might be the easiest one in the OMSCS program for me. It feels like a second-semester freshman course that's been rebranded as a graduate course. I can't imagine any other course being easier than this. (Don't get me wrong, it's easy, but it's still useful.)
Rating: 4 / 5Difficulty: 1 / 5Workload: 5 hours / week
qijT54rwyDuqUGRJr6it6A==2024-08-04T21:33:06Zsummer 2024
Special Topics: Compilers - Theory and PracticeThis is easily the best course I've taken so far. I would highly recommend this course to everyone who is taking systems specialization. It's a shame that it isn't a core course.
Content:
As with most OMSCS courses, this one is about breadth. It emphasizes both - theory and practice. I knew very little about compilers and came away with a solid understanding of various components. I think the only part that is outdated is the attribute grammar framework. The third edition of the textbook doesn't even mention it. Lectures are okay, although they mostly follow the textbook.
TAs:
The TAs are the primary reason why this course is outstanding. They are highly knowledgeable about the subject and are genuinely enthusiastic about teaching. Most of the questions are resolved very quickly. There is a fast turnaround time for grading - one week at the most and even shorter around institute deadlines like withdrawal and grading dates.
Homework:
Don't take this one lightly. The questions are quite challenging. I recommend writing code to solve them, and verify answers. For the liveness analysis, the code you write will also be helpful for Project.
Project:
In summer there are three phases - 1) Generate Parse Tree 2) Generate IR 3) Generate MIPS assembly. The difference from a full semester is that you don't have to handle floats, and you don't have to handle one register allocation algorithm. In hindsight I should have taken this course in a full semester. But I'm interested in some other high workload courses that aren't offered in the summer.
The great thing about the project is that all the tests are given to you up front. If you pass the autograder tests, and don't do anything obviously incorrect to bypass them, you will know how you perform right away. There is no submission limit, you get feedback in under a minute, and you're actually encouraged to submit early and frequently while you implement various functionalities.
There is zero starter code and a single dependency - ANTLR4. Other than that it's up to you how to implement the compiler. You don't get this level of design and implementation freedom often.
Final:
The final was a bit difficult for me. There was one significantly challenging question that I just didn't have enough time for. Also, given the amount of effort one has to put in the homework and project, the final has way too much weight. I think 25% would be a fair amount. I would recommend completing the extra credit portion in the Project, even though I didn't do it. It's worth 5 points in the finals, and gives you enough leeway for a grade.
Rating: 5 / 5Difficulty: 5 / 5Workload: 28 hours / week