CS 7643 has a reputation inside OMSCS as one of the courses that eats your semester, and having looked closely at what it asks of students, the reputation is earned rather than exaggerated. Students consistently report something in the range of fifteen to twenty hours a week, and the reason is not padding. The assignments are large because the course refuses to let you skip the hard part. That hard part is implementing things from scratch.
Before you are allowed near PyTorch, you write forward and backward passes in NumPy. You implement a fully connected network, then convolutions, then the pieces of attention. Anyone who has only ever called a framework function will find this genuinely disorientating for the first assignment and genuinely clarifying by the end of it. Backpropagation stops being a word and becomes a thing you have debugged at two in the morning, which is a different kind of knowledge.
Each assignment combines three components: code, written analysis and a paper review. The paper reviews are underrated. Being made to read and summarise actual research papers, repeatedly, on a deadline, is how you develop the ability to keep up with a field that moves as fast as this one. Most courses gesture at the literature.
This one makes you engage with it. There are also quizzes and a group project. The project is where the course either shines or frustrates, depending entirely on your team. A good group produces something worth putting in front of an interviewer.
A disengaged group produces a scramble in the final fortnight. That variance is a real cost of the format and worth planning for by finding collaborators early. On difficulty specifically, the transformer and attention material is where most students hit the wall. Reports of spending two full days debugging embedding code are common enough that you should assume it will happen to you.
Whether that is educational or merely painful depends on your temperament. I lean towards educational, because the bugs you hit are the actual conceptual confusions rather than incidental framework problems, but I understand people who feel the volume tips past the point of diminishing returns. Prerequisites matter here more than the official listing suggests. You want a prior machine learning course, ideally CS 7641 or Andrew Ng's specialisation as a minimum, and you want to be fluent in Python and NumPy rather than merely functional.
The mathematical demand is real: matrix calculus, chain rule under composition, and enough linear algebra to reason about shapes without guessing. Coming in without those turns a hard course into an impossible one. The accessibility limitation is significant for this site's readers. There is no audit path.
This is a graded graduate course inside a degree programme, and if you are not an OMSCS student you cannot take it. What you can do is use the publicly posted course pages from past semesters, which include lecture materials, as a syllabus to follow with other resources. That is a poor substitute for the graded assignments, which are the actual value. My 4.3 balances a very strong course against a real accessibility ceiling and a workload that will damage your life if you time it badly.
If you are in OMSCS, take it, take it after a machine learning course, and take it in a semester where it is your main commitment. The combination of scratch implementation, paper reading and graded deadlines at under a thousand dollars is not matched anywhere else I know of.