I don't think I have learned much from this course. The main reason is that there is a clear disconnect between the lessons and the tests (homework, project, exam). The lessons present a ton of different statistical learning techniques, sometimes going quite deep into the weeds. This could be great per se, but the lessons are extremely extremely hard to follow, and are often recitation of the mathematical formulas in the slides without much prior or following explanations. You can spend a lot of time making sure you are fully understanding the lectures and the concepts behind them, but soon you will realize that none of it gets tested. For homework you will get spoon-fed a lot of code you can basically copy paste and adapt to the required problem, the project is the same as a slightly bigger homework and the exam is a homework where you're graded for prediction accuracy. Now, of course you can still learn all the concepts even if you're not tested on it, but then I would choose better material to do so, since the lessons of this course are pretty bad.

The good aspect of the course are: there is quite a bit of support throughout the course, Prof. Mei and TAs were quite responsive and helpful. The grading is very generous, people complain about the peer review (which I agree can be awful) but in reality everything is graded by TAs at the end (I've had examples of unreasonable low peer grade without explanations that were ignored by TAs for the final grade).

I loved this course. I considered this to be a great sequel to ISyE 6501, as it delves deeper into a range of widely used modeling techniques. Like many others have noted, there is a stronger focus on writing reports and applications. I found this to be very useful, as communication is one of the most important skills for a Data Scientist.

The group project was a great opportunity to apply what you've learned with any dataset of your choice. I loved the lack of exams - instead there were 5 quizzes, and a 'take home' final, which was more of a 'solo project'.

This course covers some material covered in CDA. This course is a great alternative if you are more interested in applications rather than coding ML algorithms from scratch, but both complement each other as well. One of the common critiques of this course is that the starter code for the 5 HW assignments is in R. However, students are free to use other languages such as Python, which I used for some of the assignments.

Overall, this was my favorite course I've taken in OMSA. It was not overly challenging - it felt that my efforts went directly into truly learning. The professor is very responsive and I enjoyed his positivity and teaching philosophy.

One of the better classes I've taken in this program. The lectures are pretty much useless, but the homework and final are fair and kind of fun. You basically get a dataset, a problem, and some starter code (less code as the semester goes on) and that's it. You write your code using the models instructed, then write a paper on what you did and what you found. I enjoyed most of the assignments. There's also a project, which is similar to the homework, but you find your own dataset and use whichever models you want.

This class is easy but I'm not sure I learned anything. Lecture material is pretty bad, just him reading the slides and not really teaching anything. Homeworks were at least interesting and you get a project that you get lots of freedom on. But overall this class just isn't very good

I took this during my last semester. Probably one of my two 2-3 courses. The material and slides were very well organized, the teacher was extremally active and hosted office hours each week (and would test students on what they knew during office hours--which I thought was great), the TA's were kind, knowledgeable, and helpful. From a material and learning standpoint, while not as hard as CDA, still very valuable IMO. The general concepts of ML were done over and over again and I felt like it was a great opportunity to do a ton of practice on different data and models. It really drilled the concepts into my head. Also, technical summary is really important in this course. In the real world, if you can't summarize your work well, you are out of luck. It is a vital skill and I appreciated that aspect of the course. I will take the general structure with me. Definitely worth taking!

Cleared due to OMSCentral Owner being greedy.