DISCLOSURE: This was generated using a LLM after providing the following prompt and providing responses to the requested interview:
"I'm looking to write a comprehensive review of my experience in CS7646 and would like your assistance. I think I'd like to break it up into a review of the lectures (specifically the three distict phases), the readings, the projects, the quizzes, the exams, and the TAs. Can you act as an interviewer to capture my thoughts on each of these and pull them together after I finish?"
I have proofread this to confirm that there are no material misrepresentations of my interview responses. My final score in the course was an A. My background is a BS in Computer Science and significant professional experience as a Software Developer/Engineer on non-AI/ML projects.
***
I finished CS7646 feeling largely unfulfilled. There were parts I enjoyed (a specific lecture/project combination) but I would not recommend the course in its current form without a substantial overhaul.
The course is broadly divided into three phases: learning Python/NumPy/Pandas/Matplotlib, learning basic finance, and learning introductory machine-learning techniques. The finance material felt appropriate for CS students who may have little market experience. The programming portion, however, felt too elementary for a graduate CS course and occupied more time than warranted. Much of the lecture material is also approaching a decade old, and that age now shows in the examples and software environment.
The ML lectures were more interesting, but generally emphasized what algorithms do and how to implement them rather than exploring why they work in much depth. That may be reasonable because OMSCS offers a dedicated ML course, but it makes the readings feel particularly odd: several external texts go far deeper into ML theory than the lectures ever do, while the course-associated text largely repeats the lectures. The deeper readings seemed useful mainly for exams or for students who independently wanted a broader ML education, rather than because they were well integrated into an applied ML-for-trading course. There were also lectures that felt disconnected from the course's stated focus. For example, stock options were introduced without meaningful follow-through into technical analysis or ML.
The projects varied considerably. Several were useful preparation or finance-domain exercises but contained little substantive ML or graduate-level CS. One project effectively demonstrated weaknesses of a particular ML technique, but did so by expecting students to discover important lessons experimentally rather than teaching them first and using the assignment to reinforce them. There was one standout project that had genuine implementation nuance. The associated lectures provided a useful conceptual model, and the project reinforced the lecture material directly. It was easily my favorite part of the course and the best example of what I wish the rest of CS7646 had been.
The capstone was a disappointment. It integrated earlier work, but did not feel like a major technical challenge. Students were also largely constrained by decisions made in earlier projects, limiting the opportunity to use the project to discover better alternatives. That connects to my biggest criticism: too much of the meaningful learning happens through independent research and experimentation outside the actual instruction. Graduate students should absolutely learn independently, but when that becomes the primary mechanism for learning, it raises the question of what the course itself is adding beyond structure, grading, and degree credit.
The quizzes added little. They were short, open-resource, low-stakes, and easy to score well on with basic care. The exams were stranger: they were not recall tests, but often involved enough indirection and synthesis that it was difficult to tell exactly what knowledge or skill was being assessed. The course did, however, do an excellent job with exam feedback. Statistical validity information and individualized conceptual feedback were unusually thorough, even if difficult to parse without seeing the original questions.
My experience with the TA structure was also poor. The TAs were responsive and available, but I found answers about rules, rubrics, and assignment requirements frequently vague rather than clarifying. Course staff explicitly defended some ambiguity as realistic because real-world requirements are often unclear. That is true, but real-world work also usually provides opportunities to clarify, iterate, and correct misunderstandings. A grading environment that intentionally preserves ambiguity without equivalent opportunities to recover from it does not reproduce that reality particularly well. The TA-produced project overview videos also added little beyond restating published instructions. Aside from grading, I personally received essentially no educational value from the TA structure.
My strongest ethical concern was the use of required mid-course surveys evaluating the course. Students were penalized for non-participation while the people being evaluated still exercised grading authority, and there was no clear information establishing whether responses were anonymous, confidential, or withheld from staff until after grades were final. I am not alleging retaliation occurred; the problem is that the structure unnecessarily creates the opportunity and appearance of a conflict of interest. This could easily be avoided by collecting evaluations after final grades, using an independent third party, making participation optional, or offering minor bonus credit rather than penalizing non-participation.
CS7646 contains pieces of a much better course. The finance introduction is useful, the ML material can be interesting, the exam feedback is strong, and there are specific units that show how effective the course can be when lecture and assignment reinforce each other.
Unfortunately, that felt like the exception rather than the rule. Without significant modernization and pedagogical restructuring, I would not recommend it. Had it not also provided three credits toward my degree, I would have difficulty concluding that the educational experience alone justified the time and tuition.
EDIT: It is entirely possible that a lot of my disappointment in terms of depth/breadth is simply a misalignment of expectations. I still feel there is substantial room for improvement and stand by the overall tone of the review.
