Reviews

Pros: 1. Low time commitment 2. Most of the assignments/lectures were fun and engaging 3. Could be relevant to SWE work, at least more than other classes Cons: 1. A bit too easy, especially if you know kotlin or java 2. Many assignments, need to keep up to date (HWs are time consuming second half of the semester) 3. Detailed Review: It was interesting but not super challenging. Lectures were fun to watch but not essential to most of the assignments. If you know Kotlin or Java some of them could easily be skipped. There is no theory in this class and the assignments and home works mimic what a software developer might be working on. You utilize several public APIs and Google cloud services (auth, db, etc). I found the work load to be pretty light, but the second half of the semester there is definitely more work. If you don't prioritize starting the homework's early they will bite you. The FC assignments are rather short (1-2 hours) and assigned every week. The homework's towards the end can take 10-20 hours. The final project is completely up to you as long as you satisfy a few requirements. Overall a good class that I would recommend, but it is probably more undergraduate level in difficulty.

Pros: 1. Useful lectures 2. Doable projects/homework assignments 3. Cons: 1. Mandatory Piazza participation crowds page 2. 3. Detailed Review: This is a good class to combine with a hard one. I took this and Probability and Simulation and worked full time in a demanding job and was okay. The lectures explain all you need to know about R. You learn how to make good visualizations. Watch out for projects-- worth a lot more.

Pros: 1. lots of math, and help build the intuition and linkage with various theorem 2. good opportunity to build stats functions like t-stats from scratch 3. homework only, no exams!! grading is also reasonable can drop 2 homework Cons: 1. no practical examples 2. R code is hard to read with useless coding style xx yy zz 3. the EM Algorithm could go much further but only covered in the last 2 weeks Detailed Review:

Pros: 1. Quality Lectures 2. Made us think deeply 3. Good Professors Cons: 1. Practical assignments could have been a bit more interesting 2. Bad grading curve - need to study really hard to get a decent grade (this may be a pro for some). In my case, I forget most of the small nitty-gritty details over time. Also, it assumes you have a strong background in Linear Algebra, Stats, and Probability. This may not be the case for everyone. The goal is to learn. Having a decent grading curve would at least iron out the background gap. Detailed Review: I enjoyed this course overall. Honestly, learning is good. But if you also want to maintain a decent grade thinking that you would want to apply for higher education, you will have to work harder in this course than in other courses.

Pros: 1. Really interesting topic with a lot of buzz both in research and real-world applications right now. 2. Homeworks and Programming Assignments are short, not too difficult 3. The textbook is really good (but see notes below). Cons: 1. The class is pretty much a read through of a textbook (which is dense but very good, if you're okay with absorbing information this way and take you're time you will learn a lot) 2. The lectures are very hit-or-miss. Some provide good insights into something the book glossed over or just repeat what the book did. 3. No real satisfaction from completing the Programming Assignments. The problems you solve are either toy environments made by the course or, when you do get to use the great AI Gym library, you use the most vanilla problem. Given how many different and exciting applications there are in RL, this was disappointing. 4. Large portion of the final was dedicated to testing your skills as a human calculator rather than deep knowledge of the material learned. Many questions were similar from the homeworks but still tedious, error-prone calculations that were better left covered in the Homeworks and Programming Assignments. Detailed Review:

Pros: 1. Easy and the requirements are clear - if you know programming you'll do well in this class. 2. 3. Cons: 1. 2. 3. Detailed Review:

Pros: 1. Interesting programming assignments 2. Lecture format works well 3. Curved grading Cons: 1. Graded test cases are not provided 2. Quiz questions were difficult 3. Awkward difficulty depending on experience Detailed Review: Class workload is highly dependent on your previous experience with programming. I saw classmates that were struggling from the very beginning of the course and others that were breezing through the assignments. This made it feel like the difficulty was awkward, and certainly not what I expected from a graduate course. Too easy to be a grad-level data structures class, but too difficult for new programmers. That being said, some of the programming assignments were genuinely interesting and engaging. They were good applications of the lecture material. Some base test cases are provided to you in the starter code, but a big part of the assignment is creating your own test cases. The graded test cases were never provided as far as I know, which meant I didn't really know what went wrong with my code. The lectures have conversational segments, where a TA answers and asks Prof Lin questions during the lecture like students would in a traditional classroom. This made the lectures are approachable and the TA-conversational format works well with the course material. There were a few quiz questions in each quiz that were confusing or more difficult than I expected based on the lectures. For the most part though, the quiz questions were extensions of the lecture itself. Definitely one of those classes where you need to watch the lecture so you can do well on the quizzes. I had to skim through the lectures for a few weeks and it really affected my quiz grades. Workload was also really dependent on the project. The first 3-4 assignments were easy enough, in the range of ~5-10 hours of work each. The last two projects were challenging and took me ~12-15 hours to complete. I have programming experience, although I did not do a CS undergrad. If you took a data structures class in undergrad, you'll likely find this class to be around the same level. If you have no programming experience, I would recommend you brush up on the basics (using Python) before starting the class or don't take any other classes for this semester.

Good course overall. The first half of the course is intimidating, especially the first couple of homeworks. It gets much easier afterwards. The second half of the course is definitely more structured and easier to cope with. The course is very theoritical and only gives a bit of practice in the programming assignments.

I took this course based on the past reviews in MSCShub and due to the hype on LLMs. While the lectures by Prof Durrett were good, I feel like this course has got far better reviews than it deserves due to the active participation of the professor and TAs on Ed Discussions. Though I ended up with a good grade (A), this was a very challenging course and I was stressed all the time Pros: 1. Professor is extremely active on the ed discussion and very approachable. 2. Easier final project (the major part of the final project is about training models and analyzing the results , not coding). Average score on final project is low 90s. 3. Lectures were quite engaging and concise. 4. Very hot topic. Cons: 1. Assignments 2 and 3 were difficult and push you to the limits. 2. Midterm was quite tricky and test your ability to implement algorithms on a paper. 3. Impossible to get full credits on final project as we were told that even great projects that has little or no issues can get lower credits. 4. Need to get GPU or Colab premium Detailed Review: This course has a charismatic professor who is quite active on Ed Discussions and even replies to student questions. While his lectures were good and succinct, the assignments were a big step up from the lectures. In particular, Assignments 2 and 3 were super hard, and so was the midterms. Final project in comparison is relatively easy as the critical tasks for the fp was training the model and analysis of the results. This course is among the difficult courses I have taken in the MSCS and certainly needs far more efforts that the one mentioned in past reviews.

Mostly the course is structured around a textbook with very little instruction otherwise. If the textbook is confusing to you, the course will be very difficult. If you are unfamiliar with the topics, definitely allocate time to read the chapters more than once. The topics are interesting.