Reviews

Like the above commenter mentioned, the course is structured around the book, so if you like following textbooks, then you'll probably enjoy that about this course. I recommend fully understanding and working out the examples from the videos. Try to complete as many textbook exercises as you can to REINFORCE your knowledge. Both of these habits will pay off when you take the exam. Taking some time to familiarize yourself with either pytorch or tensorflow before the 3rd assignment hits will pay off as well. I found both the 3rd and 4th assignments very time-consuming, not having taken DL prior. Our TA, Macke was great but you may or may not have the same luck. All-in-all, the concepts were interesting and from a high-level, easy to understand, but slightly more difficult to master.

Pros: 1. One of the professors in the lectures, makes excellent supplemental lectures that are easy to follow and explain the material well. 2. Multiple choice exams. Fair final exam. 3. Interesting material. Cons: 1. The other professor in the lectures, who makes the bulk of the lectures is hard to understand and leaves out key points a lot. 2. Lots of the material makes assumptions and elides crucial details that make the point unclear (ie, assuming constraints to a problem without mentioning them at all). 3. Exams and homework couldn't be further from each other: the question styles on the exam were a huge surprise on the first two exams. Detailed Review: The first few homework assignments are harder than the rest. The exams really throw you off since the question format & types of questions are very unexpected relative to the homework and lecture material and require a lot of lateral thinking and intuition (especially visual intuition) of the material. Knowing linear algebra is a must, if you don't have a visual intuition for it, the 3blue1brown series helped me a ton. The final exam doesn't throw any curveballs so if you make sure you could do the first two exams confidently, you'll do well. Office hours were medium, hit or miss, didn't find them as useful as in other classes. Textbook was useful sometimes, but had a LOT of unrelated info. Found myself cross-referencing information with other optimization course materials I found online to make sure I understood things. Homework is very leniently peer graded because of a ternary scale. Active and responsive piazza.

Good to have a UI class in the curriculum. Teacher is very entertaining. There are short assingments due every week (3hrs each) and longer homework assignments that can sometime be very time consuming while still having a short assignment due at the same time. Plan accordingly. Content was manageable. The teacher expects you to go out of your way to find how to do things, which makes sense for a graduate level course. I found Kotlin to be frustrating as there are things your can do in certain areas of code that you can't do in others, and its not very clear on which. Overall, good course, diverse content, but cohesive and incremental nonetheless.

Mostly this is a good class. The lectures are excellent, and the five homework and one extra credit assignments are engaging and really help you learn how to apply the material. But I hated the final project. It was the single most stressful assignment I've ever had in any class I've ever taken. You're given a nearly intractable problem (given the time/space constraints you have to work with) with no guidance and left to find the best solution you can come up with. This by itself doesn't sound so bad, until you spend 20 hours debugging the C++ code of the simulator you're running in because it doesn't actually work the way it was shown in one of the class videos. And then find that your experiment results are not reproducible and you get wildly different results depending on the underlying hardware of your Colab instance. Plan to spend more than 2 weeks on it, and plan for your results to look terrible no matter what. You can still get an A in spite of that.

The content of the class was great. The lectures were very well done. The textbook is great. I didn't feel very challenged in the course though. It could have either moved more quickly, or required more challenging coding projects, or asked us to write proofs. Sai was a great TA and very responsive. The final was suddenly a lot harder than the rest of the course b/c it was only 2 hours for questions we were used to having a week to work on - watch out for the final!

Probably the easiest class you'll take. Most things are open notes/open book, and the teachers are very focused on you learning so homeworks can be turned in late and redone if you don't get full credit. I didn't find the material super easy to pick up as I don't have a strong math background, but given it's mostly open notes it turned out fine. I wish this were a theory course as there wasn't much practical info related to what we covered in RL/DL, but maybe it goes better with ML.

The first half of the course had hard Homeworks. I specifically remember the first homework being the hardest one, I skipped the 2nd one (one hw is dropped). I thought the first exam was actually pretty straightforward, and the curve was nice. Second half of the course was much much easier. The second exam seemed hard so I thought I was one of the few who did well but everyone apparently did well on it.

This course is a good one. It seems a little bit random at times, but the professor knows exactly what he’s doing. If you work just a little every week, you’ll have a decent android app development level by the end. As for the workload, be careful: This was my most time-consuming course for the first month. Then, once my mind started to wrap around this android stuff, I did the work relatively quickly. Tip: DON’T neglect the layout part (XML files). Especially “constrained” layouts! The professor trains you on them at the beginning, then hardly ever mentions them again. BUT, you’ll need layouts throughout the course.

This class is largely a companion to the textbook. The summer course is very intensive due to the shortened schedule and a chapter of the book is skipped. The homeworks were fairly straightforward from the chapters but the programming assignments were rote copying of algorithms from the text book and were not terribly enlightening. The three contigious hour long final was rather brutal and if it you approach it in the same way as the homeworks it is likely you will not finish in time; I would recommend having programs prepared to calculate some of the more iteration intensive answers. As the only test of the course it was not really possible to get a feeling for what the questions would be like and I did not feel as though it was an accurate assesment of what I had learned. I would go so far as to say that I would need to reread the relavent chapters of the book if asked to implement any of the algorithms in class. The content is good at teaching the fundamentals of reinforcement learning but I do not feel as though I am as prepared to contribute to the field as compared to another course like deep learning. Some experience with a deep learning framework is recommended for the last two programing assignments.

8 during Klivan's part of the course, 4-6 after. Material was overall very interesting, but programming assignments were somewhat lacking in depth imo. The workload and level of difficulty in this class will likely depend on your prior exposure to stats, probability, and linear algebra.