Course Details

Pros: 1. Really interesting subject matter and projects. 2. Easy to follow structure 3. Lectures made each project straightforward, since the lectures walked through an example codebase you could use for your project. 4. Easy grading 4. Not a huge workload imo. 5. AI use allowed. Cons: 1. Lots of encouraged readings, but none that were necessary to read. I didn't read them after the first week. 2. Lectures were hard to follow. 3. Final project is a little long, but straightforward and interesting. 4. Have to make slides for each project. Detailed Review: I think this is a great first class in the masters to take. There are weekly readings and lectures. I found I didn't need to do the readings and only needed to watch the lectures which involved code. I wanted to watch the rest of the lectures, but unfortunately Professor Ding's lectures can be a little hard to follow and understand. Every two weeks, a project is due, and Professor Ding usually has about 30-60 minutes of lectures going over a very similar codebase. You can just take her codebase and then adopt it to a subject you want. The projects were cool and most used public healthcare data. One week, you learned to use scipy and numpy to make graphs; then learn basic SQL; then steadily slightly more advanced ML stuff. In each project, you had quite a bit of flexibility also. You basically had to analyze the healthcare data using certain methods that were described in the example codebase, and you could analyze whatever features you wanted. You had to make slides for each project which was a little tedious, but whatever. There were two weeks were there were peer review. Then, there was a final project. You basically had to do any kinda advanced ML analysis, make slides, record yourself speaking them, and write a ~5 page report. Each step took time, but wasn't terribly difficult. And you could do it on whatever subject you wanted with whatever data you could find online (e.g. Kaggle), so it was ultimately pretty interesting. Writing the report was a pain. But the plus is that AI use is allowed, so that made it easier, especially since Gemini is integrated into Google CoLab, which we use for the projects. Once I realized I didn't need to do the readings and only needed to watch the lectures related to the project, it took me about 2-3 hours each week to watch the lectures related to the project. And it took me 4 hours to do each of the projects due once every 2 weeks with the help of AI. So on average that's 4-5 hours per week of work. Not bad. Piazza (ed in this case) was fine. Main issue was that the lectures were not great due to Prof Ding's delivery.