Reviews

Pros: 1. Very useful topic, which helps to understand better other subjects. 2. MATLAB. 3. Curve grades. Cons: 1. Lectures are too short. 2. Test cases for programming assignments are absent. Detailed Review: I am an Engineer and a Python Developer, without bachelor degree in Computer Science. It is my second course in UT Austin and only one this semester. I got “B - “(my result is 76.86% and my grade moved up on two levels only because of Curve Grades; it was a very specific Curve – only for ‘B’ and ‘C’). Start course: 192 students HW1: 164 students, mean - 12.23 median - 14 from 14 points Midterm 1: 136 students, mean - 61.35 median - 61 from 100 points Midterm 2: 122 students, mean - 87.29 median - 95 from 100 points Final Exam 2: 118 students, mean - 58 median - 57 from 100 points “The course average is 83.4975 84 students have a course grade of 80 or better, which is 71.19 percent of the class. 25 students have a grade of 90 or better, which is 21.19 percent of the class. “ Prof. Nicky My preparation: 1. LAFF (from edX). 2. YouTube lectures (MIT 18.06 Linear Algebra, Spring 2005 Instructor: Gilbert Strang) - 10 first lectures. 3. “Numerical Linear Algebra’ Trefethen-Bau – Part 1. What would I add now: 1. Watching proofs from LAFF(Margaret) more attentively. 2. Refresh Calculus (limits, derivatives, ...). 3. MATLAB Onramp – it is a free course. Also, it is possible to register an account under Austin’s email and get all courses under their license free. I did not have ‘Linear Algebra’ course in my local university and was not familiar with formal proofs, so this course was very challenging for me. 1. LECTURES. They are too short for these complicated topics and can help only people with strong ‘Linear Algebra’ background. 2. “Numerical Linear Algebra’ Trefethen-Bau (was recommended for this course) – very good book and covers all topics except Week 8 ‘Descent Methods’. 3. Book ‘ALAFF’ contains many useful examples with proofs. I tried to work out the explanations. 4. Understanding algorithms through its MATLAB implementation is the best way for studying, unfortunately, I understood this lately. 5. Week 1 – 5, 9. MIT lectures (Gilbert Strang). 6. Week 6. ‘Numerical Stability’. YouTube lectures from Natan Kutz. 7. Week 8. Descent Methods’. YouTube lectures about optimization from Michel Bierlaire. ‘Linear Algebra and Optimization for Machine Learning’ Charu C. Aggarwal 8. Also, I would recommend YouTube lectures from Martijn Anthonissen for the last weeks of the course, 9. I tried to find videos with examples calculations on the matrices and it helped me a lot at Midterm1. Algorithms with examples are better than only theoretical explanations. 10. MATLAB assignments are without the test cases. It is very uncomfortable. Only one test case could solve many problems for a student. (Input/Otput? In row or column vectors? Edge cases? Matrices dimensions? and so on). I hope that auto grader for programming assignments will appear in the future. 11. LaTeX is not an obligation. I preferred hand-writing. 12. GRADING. More than generous. ‘Lack of work’ is better than ‘Missing’. 13. REGRADING. Some people improved their scores. 14. COMMUNICATION. Discord Server. Ed Discussion – active response from TA. Not many people at OH. Youngran is the best TA on this course. 15. Midterm 1 – closed notes, self-proctored, timed. Enough time for answers. 16. Midterm 2 – a week for completing. The third programming question was very challenging. 17. Final Exam – 10 days for completing. Harder than Midterm 2. All tasks were about the third part of the course. 18. About the statistic from Spring 2023 (with so many ‘A’). Those students were very lucky! Course is really tough! “In spring 2023, as you can see, there were significantly more students. But there were the same number of TAs. So more assignments had to be graded for completion that semester rather than correctness. There just wasn’t enough staff to grade everything completely. And that boosted grades a lot.” Prof. Nicky 19. This course can be taken as the first course in MSCS program. I hope that my review will come in handy for future students. Good luck!

Pros: 1. Pretty straightforward assignments/tests 2. Great & organized lectures and course notes 3. Responsive TAs/LFs Cons: 1. Some repetitive plug and chug in R 2. 3. Detailed Review: Overall, I really enjoyed this class. I have been dreading taking the more stats-heavy classes in the curriculum but this was a nice surprise as it was a new class added this semester. The lectures and accompanying notes are really well done and organized. There's quite a bit of lecture content to watch, but I don't think it was super necessary watch them all if you just want to read the notes. Well documented starter code and R-code walkthrough lecture videos as well. The homework assignments were designed to take you 60-90 minutes and that was pretty much on-point or an overestimate for most of them, and you get one drop on top of that. The 1 midterm and 1 comprehensive final were untimed and open notes but you had a 48 hour window to complete them. I wish more courses in MSDSO had untimed exams as they were significantly less stressful and you could actually check your work. I think this course would probably pair well with any of the other courses that don't have tests like DL, regression, APM, and data viz even if you are working full-time. Taking this course over CI for elective group 1 is probably a no-brainer as this course also contains a whole section on CI and is likely way easier (just based on the reviews, I haven't taken it). The TAs/LFs on Ed were really on top of things responding to questions most of the time within minutes of being posted. I thought the reliance on various R packages for most of the later segments got repetitive. The first few segments had some hand calculation examples which I thought were really great at making sure I understood what was going on under the hood of these packages. I just wish some of that was carried forward to the later half of the course. Also, I think it would be great to show more examples outside of the healthcare space as I'm sure most students are not in that industry. I like being able to attempt to apply what I learn during each course at work but I didn't really feel like anything applied for me (I don't work in healthcare/medicine). Maybe adding a lecture section showing some applications outside of healthcare would be cool.

Pros: 1. Really engaging lectures by Professor Lin 2. Professor Lin and his TAs are on Piazza often to answer our questions 3. Office hours are very frequent, usually as often as once per day (on Zoom). This is better than what is offered for on-campus courses; indeed it is a gold standard of how online Masters's courses should be run Cons: 1. The quizzes could be vaguely worded at times 2. The learning curve at the beginning of the course could be tough for those without prior Python experience (at the very least, some experience with OOP and recursive functions) Detailed Review: As someone who has prior experience in Python I really enjoyed the course. The lectures are engaging and the homework is suitably challenging. That said, I think people who have no prior experience in Python would find the beginning of the course rather difficult. As I understand it, the course will not be offered for 2022 Summer and Fall for revamping. Honestly, I think the course is fine as-is and is of a suitable level of difficulty for an MS in DS. Nonetheless, the MSDS program should perhaps offer another (more introductory) programming course for those who are less experienced in programming; I sense there is a large divide in the class between people who have coding experience vs those who don't. Perhaps something could be done to close this gap (say a non-credit programming basics course)?

Pros: 1. You choose to put in as much (or little) effort as you want 2. Low time commitment 3. Final project is open ended and you can do whatever you want 4. Best TA staff in MSCSO Cons: 1. Quizzes were too easy 2. Lectures are too text heavy at times 3. Prof's accent is tough to understand at times Detailed Review: I thoroughly enjoyed this course, as it gave me the time to explore various applications of ML techniques and finally decide to work on a case I thought was interesting. The head TA, Jun, puts in a lot of work and is easily the most honest and responsive TA in the entire MSCSO program. Kudos to him. His catchphrase "don't forget to practice" was my favorite part of the lectures ;) This program helped me balance an otherwise tough period in life due to its low workload and lack of deadlines. I am surprised by the negative reviews for this course, given that it has been said multiple times on the hub that this is a low workload class and the course staff never advertised it to be a very deep course. Summary - take this to knock off an easy credit, or if you want to pair this with a tough subject, or are having a rough time in life. Don't take this if you expect the course to teach you novel techniques.

I took this course during the summer since it was supposed to be the lightest workload course, but the result was still pretty stressful. Pros: The content is very interesting, and not super challenging. Cons: Fast summer pace, very reading-focused course, and a lack of good support for the neural network components of the programming assignments. This last one wasn't actually an issue for me (I used the Keras API, which I STRONGLY recommend assuming it's still allowed in future iterations), but a lot of people using pytorch or tensorflow's lower-level API really struggled. Final exam was really hard, mostly because of the format (input boxes, no partial credit, submit once and pray you rounded right). Still got an A, but wished I had not taken it during the summer. Difficulty definitely ramps up in the last 4 chapters (function approximation, neural nets, etc.).

The course was well organised and the instructurs and TAs were very active on supporting our learning. I like the fact that they emphasize learning more than anything else. Since I did not have a strong math background and am new to linear algebra, the materials were quite challenging, especially toward the end. I wish I studied more to get the most out of the class, but it was very hard to catch up with the pace of the class due to my full-time work and taking another course at the same time. This was my very first semester for MSCSO for me, so if anyone new to MSCSO and is willing to maximise the learning other than finishing the program asap, I recommend starting with 1 course in your first semester to see how it goes.

The textbook is awesome and the projects were great and I learned a lot. The lectures were kinda rambly and just summarize the same content in the textbook. Honestly, just skip the lectures and read the textbook. The professor is very responsive and helpful on Piazza which also helped clarify a lot of things. I found this course easier because I already knew C and Assembly, I recommend brushing up on your computer architecture before taking this course.

I really enjoyed this class. I thought the material was rather straightforward, the book is great, one of the TA's was awesome....The only complaint I would lodge is that the video lectures from professors are pretty useless, in my opinion. Just read the book, start the programming early enough to debug, and pay attention when you're doing the homework and you'll be fine. The final was long, but there wasn't really anything on it that I felt was unfair.

Class is well organized and well taught. Instructors are engaged and active in discussion with students. They genuinely care about helping students learn and the focus is entirely on that. However, on a subjective note, I actually did not enjoy this class. There's nothing wrong with it, but the material that is covered was not very valuable to me given my personal and professional goals. This is first and foremost a class in NUMERICAL computation. The focus is on algorithms that implement floating point linear algebra computation. If you're taking this class early in the program then it's a great soft introduction to or refresher on proof techniques that are important in other courses. If you're near the end of the program though, it's not super valuable unless you care about the details of implementing numerical LA algorithms for its own sake. If you're going to take this class, I recommend taking it early in the program and not later since you'll likely get the most benefit out of it then.

Thoroughly enjoyed this class. Time consuming in the beginning as you are getting used to kotlin and how to make android apps. First few assignments were time consuming for me because of this. No exams, only programming projects and weekly homework’s. Final project is open ended and you learn a lot from it. Highly recommend this course.