STAT 4630

I personally love Professor Woo. In my experience, it is rare to find a statistics professor that is both a good professor and knowledgable in the subject. Woo is very straightforward, he is explicit about what will be on tests and what he expects on each assignment. The group paper is a grind at times, I would recommend choosing a topic that all the group members understand so there isn't a knowledge barrier at any point.

Overall, the content was interesting and Woo is the best.

This class consists of homework assignments (roughly every two weeks), labs (once a week or so), two midterms, and a final project. The labs and homework assignments fully taught me the material, as Professor Woo's lectures are not helpful, expect for the fact that his posted lectures are fill in the blank and he fills them in during lecture time. Don't bother reading the textbook. The final project is an easy grade boost that you work on all semester. Midterms are graded inconsistently and are brutal. Think twice about taking this class if you are not a Stat major!

This is probably the best STATs course I have ever taken at UVA. Professor Woo did a near perfect job putting this course together. The material is by no means easy but Prof. Woo gives assignments that are really efficient in terms of helping students understand the material without being overly difficult and this made all the assignments really manageable. Compared to other STATs courses this is not always the case.

There were maybe 4 times throughout the semester where we had a couple assignments due within the same week which was a little stressful. If you followed along with the material in the HWs and labs well enough the exams were very fair and they are only worth 15% each so you don't need to do great on either of them to end with a good grade.

I can't recommend this class enough and I got to give huge props to Prof. Woo for creating such an excellent class!

This class is the next step in the journey that all Stats majors have to take. This shouldn't even be an elective (it's actually now required for the new BS), machine learning is just so useful in the Statistics field. Please take it, if you want to go into Stats.

In terms of the course itself, it's a lot of work. There's a semester-long project that is 40% of your semester grade. On top of that, there's a few homework assignments sprinkled throughout the semester (we had 5 for my semester) worth 20%, and one or two "labs" per unit (we had around 7 units) at 10%. However, don't be intimidated, very little of this work is actually difficult, it's mostly checking that you paid attention in lecture.

In more detail, the grading is:
40% Project
20% Homework
10% Labs
15% Midterm 1
15% Midterm 2

The midterms were the hardest parts of the course imo, but they're still very doable, since they're open note.

The material Prof Woo covers is in a very applied scope; he provides a few cases of where to use the concepts, and the homework uses datasets found in R. Very little theory was covered, so if you're looking for that, then you're out of luck.

Woo was a great professor. I found he held our hands a little too much sometimes, but I prefer that to profs who don't care.

Overall, a really great class!

#tCFfall2021

This was a really good class if you're interested in ML. It didn't go too deep but was a good overview of different statistical learning methods. If you want something more in-depth and mathy, I'd recommend the CS department's ML (but it is heavy on coding). I personally preferred this class better.

Woo did a good job of explaining the material but his lectures were mostly just him reading off the slides (which he posts). However, for some reason he leaves the bolded keywords off the slides he posts, so you still need to watch the lecture recordings to figure out what those words are.

Based on the other reviews, I guess he got rid of the lecture quizzes so the assignments were just lab quizzes (4 out of 12 were dropped), homework sets (1 out of 5 was dropped), two midterms, and the group project. Like the other reviews mentioned, none of the assignments are difficult and he gives us plenty of time to finish it, just make sure you get started early. The midterms were in-person but open-note. They were fairly similar to the homework/lab questions where he gives R output and asks us to interpret or true/false questions where we need to explain our answers. It was completely short-answer but I don't think they were too difficult or much of a time-crunch (I averaged a 92.5% on them). The homework sets/lab quizzes also aren't difficult and he's really helpful in OH if you want to check answers. I would make sure you have a friend in the class to check stuff with, though.

Overall, a fairly easy Stat elective if you're willing to put in the work, but you will learn a lot!

#tCFfall2021

Just heads up I'm really slow, so anything said here is probably true for only a few of you. The class is really introductory to machine learning techniques. Prof. Woo would say what things are, but seeing that you don't know linear algebra as a pre-req, he would skip over the lin-alg explanations and show you how the algorithms are applied. This may be fun if you want to apply ML techniques to datasets immediately, but if you want to go deeper into the topic, you need something else. I thought the course was good enough. Sometimes it was alot of work, but this was only for the weeks where we had an exam or a project report. Prof. Woo is a good professor. He's very clear about how he wants his work to be done, and really helpful in office hours. Also the grading was very lenient, and the exams were mostly memorization. I would recommend this class if you're a person interested in stats.

This class was good and included a lot of applicable material. I won't lie, this class is A LOT of work and you must stay up to date with the work. Most, if not all, work will be due on Fridays at 11:59. If you leave everything until the last minute to get started, you will feel overwhelmed and stressed. There is some combination of lecture videos, lecture quizzes, homework sets, or lab quizzes due during the week. You CAN do well in this course. Prof Woo is incredibly forgiving in his drop policy and gives you more than enough time to complete assignments. He makes himself incredibly accessible to answer questions during OH and is very approachable. The weighting of the class skews towards the semester long project, homeworks, and tests. If you utilize all the material he provides in his lecture notes, numerous examples in labs, and his code given, there is no reason you cannot succeed in this course. By staying up to date in this course, I was able to take a breather in finals season and end with a comfortable 96% in the course. You can do it, I do not consider myself by any means to be super smarter than the average UVA student. If I can do it, so can you!

This is a must-take stats elective. The material covered is foundational to higher level machine learning and is a great gateway to the field. Woo presents in such a way that the machine learning methods covered become intuitive and practical. Woo is a really great lecturer who does a great job at explaining the material. The one possible downside to this class is that there is just a lot of assignments: lecture quizzes, lab quizzes, homework, and a semester long group project that is 40% of your grade. However, none of these assignments are particularly difficult and really just check to ensure you paid attention in class. The two midterms (no final exam!) were take home this semester and very easy. This course is nice because it allows you to take the graduate machine learning class and gives you a lot of R experience. I'm not sure if a regression course and R experience are required, but I think you should probably have taken some previous stats courses (ideally probability + math stat) + a regression course if you want to really understand the material ( not that it would be impossible to succeed otherwise). Overall, this is a great course taught by a great professor!

#tCF2020