Course Details

Pros: 1. Well structured course 2. 3. Cons: 1. Horrible staff support. 2. Cutthroat grading scale. 3. Detailed Review: This class is great for creating a strong foundation in linear algebra. The course is very well laid out, but I would not recommend taking it in the summer as it seems like it was definitely structured to benefit from the longer fall/spring semesters. I don't think the course has changed much in the past few years - there are a total of 9 (weekly in the summer) homework assignments (2 lowest dropped), 1 proctored proof-heavy midterm, 1 take-home coding midterm, and 1 take-home coding final. You can expect a flipped-classroom-like setting for most of the course - the lectures can sometimes be short and instead guide you to proof a lot of the content to yourself through "homeworks" from the text. Overall, I think this works well for understanding a lot of the content. The bad: the staff support was horrible for this class. Questions on Ed discussion were completely ignored by the staff, even if they were asked weeks in advance. Not completely unheard of, but the grading can be very cutthroat - you either get 100% for a perfect answer, 50% for a small to semi-large mistake, or 0% for a completely wrong answer. It seemed that sometimes the "perfect" answer meant you had to proof things the exact same way as the key the TAs were given - no alternative proofs. These things combined made for a horrible experience with grading, as you could easily lose points off of your overall grade by small mistakes. Overall, this class can really benefit from revising its grading structure and staff support.

Pros: 1. Nice book 2. Nice professor 3. You will learn a lot, and develop your solution skills Cons: 1. Bad TAs 2. Exam gradings poorly weighted 3. Detailed Review: I took this class as one of my first classes in the master's, and I loved the content and the challenging way it motivated me to improve my math skills (Informatics Eng. major) and I have already seen part of this. The only "good" TA solved some doubts and kindly helped us via edstream, but for example, in the first exam, it was not difficult at all. Still, they had the rubric as 6 points (excellent), 1 point (minor mistake), and 0 if the point was entirely wrong, which does not make sense a lot, because I (and some other students) did like 95% of the first and second points good, but only got a 1 instead of a 6 for a "mistake" that did not was a mistake because they "preferred" a way to show the results. Also in the first exam, one point was about a demonstration, but you must use the demonstration that the TA wanted, if you used another one you got 0 even if you ended up with the same solution. The final exam was "easy", but we all got wrong a point. After discussing with other students we found the answer in a referred book that Professor Robert mentioned in the course. We found that it was the same as many students ended up with, and after showing the evidence to the TA he said that it did not make sense and gave us 2/10 points instead of 0/10.

Pros: 1. Great textbook 2. Detailed summary of each chapter 3. The sheer amount of homework keeps you engaged in the class by default Cons: 1. Crammed timeline for summer 2. Uneven and sometimes straight up unfair grading 3. Abysmal TA support Detailed Review: Don't even think about taking this class unless you have a strong understanding of undergrad linear algebra AND Mathematic Proofs. It is honestly shocking to me that there is not a prerequisite requirement through UT that students have to take before being allowed to take this class. As most other posts from this semester state, the TA support was essentially not existent. If questions were asked on EdStem, they were more than likely answered by another student. I got more information from a student made discord than the official discussion boards of the class. Midterm 1: This is probably a top 3 hardest exam I have taken. There is so much information that you have to have memorized going into this, that it is almost RNG whether or not you will remember the exact algorithm or equation needed for one of the four to five questions (which is why undergrad linear algebra is an absolute must have). To study, they gave us a practice exam, which was really helpful. The funny thing is that they gave us zero answer key, and when asked about it the TA's response was essentially "figure the answers out yourself". Super helpful and not at all lazy! Personally this led to marks being taken off because my responses were not in the format that they expected. Speaking of marks being taken off, do not even make a small mistake in any of these exams or homeworks or else you will be punished heavily by the grader. Im talking 10-20% for small mistakes. Homeworks (proof/coding assignments): I found these super uneven in their time commitments. During the two weeks of the class, the assignments were an absolute dog fight to complete. The first homework was due the Sunday after the semester started (which was on a Wednesday). Expect to spend minimum 10 hours on the first 4 homeworks each. After the first midterm, the homeworks tend to be a little bit less intense, small proofs and snippets of code for the assignments, which was honestly a relief. The harsh and intense grading continued in these homeworks. There was almost never a real rubric for how we were graded. I remember for one homework we were supposed to "investigate" a specific algorithm. That was essentially the depth of the rubric. When I received my grade for that homework, I was told my investigation was not to their standard. What the standard was I have no idea, but I essentially received a zero because they didn't think it was enough. Thankfully the lowest 2 grades were dropped out of these, which was relieving, but the harshness of the grading and how rushed it all felt just ended up feeling like a complete mess. Midterm 2 and Final: These were fine, open note and untimed. We had more than enough time to complete them (like 7-10 days or something) and the actual content of the midterms were fairly simple. The big gripe that everyone had was for the programming questions there were zero test cases provided by the staff. So hopefully there wasn't an obscure edge case that your code didnt take care of! Some of these algorithms calculate to 10^-7 decimal places, so knowing about these would have been helpful. When asked about it, the staff told us to write our own test cases and that they couldn't tell us how strict they would be on our answers (which essentially meant no rubric). Overall: I am not joking when I say that this class had the worst TA support not only in this program but in any college class I have taken. The textbook/ class was written and taught by a professor that has since retired, but the videos are the old ones from years ago. So you are being taught by someone that is not even on staff anymore, which just blows my mind. This course should just straight up not be offered for summer again. It is too rushed, crammed, and hectic. And if it is, they really should restructure it in a better way. Hopefully the OMSCS programs administration sees the grade distribution (23% of the class did not receive a grade high enough to fulfill the theory credit) and reviews and does something about it. If you do not have the prior knowledge, just take another class for the theory credit and avoid this.

Pros: 1. Material could be applicable for more math heavy concepts in CS 2. Lectures align with the textbook 3. Cons: 1. LF/TA support this semester was horrible/nonexistent 2. Not everything is really explained 3. A lot of proofs end up being hand-wavy Detailed Review: There's nothing extremely wrong with the class in itself. It covers a lot of material and it does a good job explaining the basics. I will say I'm not particularly strong in proofs so I definitely struggled a bit in the beginning of the class. This leads to the negatives of the summer semester. This class was not made for the summer and I would highly recommend you don't take it in a short semester like the summer. Homeworks and lectures are due the same week that midterms are due. Even though the class recommends that you have a strong background in proofs, I still think some of the proofs could have been explained way better. There were so many instances where the proof explanation was literally something like "algebra" or "these are equivalent" without anything else. Now this wouldn't be a huge problem if it wasn't for the fact that this program was made for students who are probably full-time and have probably been a while out of school. I understand that understanding proofs for this class is important, I'm more just asking for a few better explanation. The lectures are explained relatively well but I think theres a few shortcuts that are taken and a few concepts/proofs/questions that are never really answered so many students ended up being pretty confused. Now for my biggest gripe. TA/LF support was more or less nonexistent. I tried going to office hours at least once a week and besides the head TA, I've never had any of the other TAs actually show up. I've never seen an edstem board so dead because I think most students realized that the TAs don't really answer your questions. The first few homeworks had relatively clear criteria but some of the graders don't really bother telling you why you got points taken off. As you go further in the course, the homework stop having clear criteria. I literally got points taken off because one of the homeworks was "implement a function" and I did it correctly but they didn't like the way I initialized an array so I got points taken off. Same as earlier in the semester, there's barely any explanation on why you get points taken off. The grading between the TAs are super inconsistent and its almost like playing roulette. It almost feels like if your answer doesn't exactly match the answer key, you get points taken off. As I said, there's not a ton inherently wrong with the course itself but the fact that it's offered in the summer when it's definitely not meant to be a summer class as well as the horrible TA support made the class pretty painful.

Pros: 1. Covers a generous amount of linear algebra, basics and advanced 2. Material is well presented in the textbooks 3. Cements a great foundation for later courses in the program Cons: 1. TAs will make or break your grade 2. Lectures often end with some form of "prove this to yourself without me explaining it" 3. Two midterms and a final can leave you playing constant catch-up if you work full time Detailed Review: First the good: This will set you up for success with future classes that rely on LA for concepts and applications. The course itself is well designed, even with an index that references all of the terms and definitions covered in the course. Assignments are the honor system where you can easily click through them without doing them. The course itself is free on EdX so you could even do it on the side with another class before taking it for the credit. I enjoyed this course at the beginning, when the TAs weren't required. The material was difficult at first, but quickly became easy to grok on second readings. If you have any issues with writing proofs this is a good spot to practice and receive feedback on how to improve. This leads to the bad, and the bad quickly overwhelmed the course halfway through: This feedback loop of course depends on your TA selection. I'm not sure if TAs are required to make sure a certain percentage of the class does poorly, but in this semester it seemed that way. I would actually describe the grading and feedback as hostile. How I mean is that either you would lose points for minuscule things, or lose points because you didn't meet some criteria. The criteria was often unknown until after grades came back, where the TA would leave a single half-sentence describing what you missed. One of the most notorious examples of this was for one homework asking you to investigate a certain criteria and compare different methods of convergence. A large number of the class lost points because they didn't give the exact value required for optimal convergence, which was not mentioned anywhere in the homework. The homeworks themselves are between 0 and 3 points for each question so missing a single point can be catastrophic. Usually you would expect at least a 1 for putting in effort on an assignment; the TAs in this course made sure you got a 0 instead, as if you didn't even turn in the work. Requests for regrades could then be redescribed as requests for being told you're just beating your head against a wall. In a normal class, you could take the feedback on where you did poorly and improve on it in the next homework. Our class we had homeworks essentially all graded near the end of the semester, to where homeworks we've submitted could have been done differently had we had the feedback of previous homeworks by then. Don't expect a curve either. Despite previous semesters all reporting generous curves, or at least enough to bump them up one category, our shortened semester had zero curves. Grade distribution as a result led to 25% of the course theoretically being placed on academic probation. Any requests for a curve were either ignored, or the requests deleted by the TAs. This was devastating as the curve could have been less than 1% to make a letter grade difference to a chunk of the class. In summary, it is a good class to learn advanced linear algebra techniques. If however you have any doubts in your proof writing ability, or your theoretical math, take the course on EdX for free and save yourself the stress. A bad TA could be the final decision on if you pass with a credit, or you fail the program.

Pros: 1. Textbook and Lecture videos are decent. 2. Depth of content is good 3. Cons: 1. Worst course support in the program 2. Requirements for assignments unclear 3. Expect your GPA to fall after taking this class Detailed Review: It's hard to ascertain if this was the course or the horrible TA and instructor support during the summer offering. In contrast to the Spring or Fall, this course in the summer moves extremely quickly. Every week there are a set of reading quizzes and a proof/programming assignment. In addition, there are two midterms and a final exam. The weeks of the midterms and finals, you additionally have to complete (rather challenging) proof or programming assignments. This leads to a rather high workload throughout the course which feels like a constant sprint. The textbook is pretty dry as are the lectures, but they are highly informative. Lastly, the assignments are incredibly poorly defined in terms of requirements and clarity. Starting the summer course, TAs notified the students that there would be office hours on Wednesdays and Thursdays at 1:00 PM CT. When asked if there could be any modification to the times or recordings of the office hours for students that work full time or are international, the staff response was a prompt "get f*****". This ended up not mattering after about 6 weeks, at which point the TAs just stopped showing up to the scheduled office hour slots anyway. Lastly, to add insult to injury, when the staff was asked for practice materials the answer was "refer to the textbook". The only exception was midterm 1, where a practice midterm was provided. The TAs did not release solutions and instead instructed students to discuss the problems with other students on ED discussion. This was fairly frustrating, as the entire first 1/3rd of this class is learning a very specific syntax and reasoning method that the instructors want you to use via trial and error on the first 4 assignments. This would have been made better with ED engagement or support, which ended up also being non-existent. The professor was engaged on ED for the first week of the class, after which he disappeared from the course. The rest of the course was run by a TA. In the entirety of the course, there were a total of 40 ED posts and responses submitted by the TAs. Out of these, 18 were grade announcements, office hour cancellations, and date changes. The remainder were generally snarky posts from a single TA about why they were stupid for not uncovering his hidden test cases in the midterms and assignments, reminding everyone that "yes, it makes sense that if you pass 98/100 test cases your score on this problem should be 15/30", or finally, letting everyone know that "yes your Matlab function should deliver a correct solution for an input matrix that is 1x1 in size." This was followed up by the TAs leaving the students with a "no, this course will in no way be curved" and then archiving the ED forum. Lastly, the grading was weeks behind the entirety of the class. The final 2 assignment grades, which covered the material of the final exam, were released three days before the final exam was due, as were the grades for the second midterm. Students learned their grade going into the final roughly a day before the Q-drop deadline, which many students used. With regards to the curve and grading distribution, the following is the distribution of grades for the 93 students who did not withdraw from the course this summer: A or A-: 32/93 B+, B, or B-: 40/93 C or lower: 20/93 This was sort of a kick in the teeth for those who did poorly considering that in comparison to previous semesters, where 93% of students were receiving a 3.0 or higher, roughly 35% of this class has now received a grade that could conceivably qualify them for academic probation and 20/93 did not meet the credit requirement. The following were the distribution of grades for the bundled assignments, midterms, and final exam: Homeworks: Q1: 77%, Median 86%, Q3: 93% MT1: Q1: 46% Median 63% Q3: 82% MT2: Q1: 78%, Median 85%, Q3: 93% Final Exam: Q1: 65%, Median: 80%, Q3: 85% TLDR: This course is probably okay during the spring or fall, with robust course staff support and a slower pace. In summer, where the quality of instruction is noticeably lower, don't touch it with a 10-foot pole. That is unless of course you enjoy teaching yourself graduate level math directly from the textbook and have all the time in the world.

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. Lectures and Course Website are well made and easily accessible 2. A decent chunk of your grade is based on HW and Edx completion 3. Provide a lot of good external material to learn from as well Cons: 1. Course is no longer taught by the ones who made the lectures, making it seem oddly disconnected from the rest of the course 2. T.A support is limited to nonexistent, they also don't seem eager to help students learn 3. A lot of the questions are phrased and structured in a way that makes them seem open ended, but the TAs themselves only grade in one rigid way Detailed Review: Overall the course begins with a ton of useful information, but that goes along with a ton of HW and assignments. It can be difficult to get all of it done, especially if you're working full time, however the material itself is well presented and intuitive to learn. The course slows down tremendously after the first midterm, but that coincides with the material being less generally useful and very specific towards implementations of specific algorithms that don't seem useful to other classes/material we'll probably see in the future. My biggest gripe with this course is with the Final and the TAs/professor. The difficulty of the final is considerably higher than the previous assignments (especially the programming assignments) and a few of the questions are so confusingly worded that it leaves you spending more time thinking about what specific, narrow answer they want out of you rather than actually trying to engage with it in a proper way. This isn't helped by the TAs who could probably just be replaced with robots at this point. They respond with either one word or one sentence answers and will not hear you out at all if your answer is even slightly different from what's written on the answer key they were likely given.

Cons: 1. Midterm 1 and Final Exam are incredibly difficult 2. There are no given test cases and autograder for programming assignment 3. Some of the theory homework have ambiguous instructions Detailed Review: There are no given test cases and autograder for programming assignment, other courses always provide test cases and autograder which we can use to test our implementation. There are some theory homeworks that have ambiguous instructions that can lead you to give a wrong answer although it is not your fault, no matter how valid your argument, the TA won't change it. The close note midterm 1 is very far from the practice and what you've learned. Final exam contains programming and theory, but likewise, they don't give you test cases or autograder, and the theory have some ambiguous instructions.

Pros: 1. I feel like I learned some from this course and it might end up being useful. 2. Rigorous homework so you can learn from them. However, exams were much harder. 3. Free points via Edx homework, which are crucial to remember to click through. Cons: 1. Exams were very difficult. 2. Edx support was limited at best. Most of the time, we were given little direction with what was expected, especially on the exams. 3. Professor is only an Edx facilitator, there is no instruction offered. Detailed Review: I have been very disappointed with this course. Going into it, I expected it to be an easier class in the program. However, with the new instructor, things have changed. The average on our first midterm was an F. For a program where a B- average can get you kicked out, I find this unacceptable. The exams also adhere to an "honor system", which is equivalent to an underage drinking law in a small town in the South. It simply doesn't work. That is why the distribution of the second midterm was either bimodal or significantly skewed. I do not have many more things to say, but I will caution with this: If you are new to the program, consider taking either only this class, or do not take this until further in the program. I know it is a good precursor to ML, DL, and RL, but if you do poorly, you could be setting yourself up for an early exit in the program.

General Thoughts: This is not really a theory course. Contrary to what others might say and how UT allows this course to satisfy a theory requirement, this course doesn't require that much mathematical maturity. Elementary familiarity with proofs is sufficient. If you don't have that, spending an hour reading primers on writing proofs should be more than enough. TAs were not harsh if proofs or reasoning were not so rigorous. The text is frankly also not that rigorous. The vast majority of the course is implementing algorithms and routines sketched out in the text. Pro: You can complete many of the assignments without in-depth knowledge and understanding of the majority of the subjects in the later weeks (week 5 onward). Con: I was able to complete most assignments and exams without learning much of anything. Instruction: There is almost no interaction between the professor and students. All of the lectures, notes, and assignments came from the ALAFF text. We had one hero TA (Youngran) who answered 99% of the questions on Ed. I was not personally a fan of the lecture videos nor the text. I found them to be lacking in clarity with lots of hand-waving. This was especially true of later weeks where topics discussed the mechanics of specific algorithms but without good and clear explanation of why any of it was important or the motivation. One major gripe i have about this course has to do with organization. Instructor and TAs had key information related to assignments/exams/course generally spread out on Canvas (either buried in the assignment description or in announcements) or in the Ed Discussion forum. While there was ample support from one TA in Ed, instructors/TAs should figure out how to allow students to tag questions with Assignment # and/or Exam, making it easier to filter threads to find information. This would also reduce question repetition. Also, if something is a requirement for a specific problem or assignment, it should not be buried in an answer to a question in Ed. It should be explicitly written in an assignment PDF and/or the instructor should be emailing the course to make it clear.

Pros: 1. Extremely good video lectures 2. Theory heavy, but the theory is the baseline for almost all advanced computation 3. Still has applied programming Cons: 1. Front-loaded difficulty curve makes it difficult to pair up with another course at the start 2. It is still Matlab (which we get a university license for, but still... index starts at 1) 3. Honestly, not many cons, and only the first one is meaningful Detailed Review: This course has the best lectures of any of the courses I've taken so far. The professors cadence and environment just worked for me. Maybe I'm old-school like that. Also, if you are uncertain if you are ready, the undergrad prep is available for free online

Pros: 1. Topics are highly relevant to Optimization, ML and many other areas 2. Course authors structured the contents very neatly 3. Head TA was very helpful Cons: 1. Lack of ownership from current staff 2. Some of the topics covered are very esoteric and useful only in scientific computing 3. High pace in the first few weeks Detailed Review: This course should be taken as first course in the program by those who are not familiar with LA beyond vectors and matrices. Without understanding norms, semidefinite matrices and eigenvalues, you cannot learn much of machine learning theory. However new staff were not fully into the materials yet, that was my experience. However towards second midterm and finals they realized this somehow and graded more leniently. I somehow felt that I did not need to put that much effort into painfully solving the last questions in finals, because they were freely awarded due to lack of time for staff to evaluate 300+ submissions.

Pros: 1. Textbook/website is well organized and detailed 2. Instructor and TAs were responsive and helpful 3. Explains well how linear algebra relates to other CS concepts Cons: 1. Can be time consuming 2. Uneven assignment difficulty 3. Detailed Review: I enjoyed this course overall and thought the content was well-presented and extremely relevant for those in this program. I have a strong math background (my undergrad degree is in math), so I felt well prepared for this course despite it being my first course in this program. I think some of the kinks from Spring 2023 have been worked out -- grading was not too slow for Summer 2023 in my opinion, with assignments coming back around 1 week after they were due. TAs were present on Piazza and most questions got answered within a day or so. The textbook and lectures were awesome and the assignments directly related to the chapter content, making them not too difficult to figure out most of the time. However, because it is a proof based class, the weekly homework could sometimes take a significant amount of time. Some weeks, I spent just 5 hours on the course but other weeks it was up around 20 (the week of midterm 2 was pretty brutal time-wise). The end of chapter assignments were visible all semester, so one could work ahead to mitigate this. Overall pretty good, just be prepared to have a few very long/more difficult assignments that may overlap with exams and plan accordingly.

Pros: 1. You will learn a lot about how fundamental computational linear algebra algorithms work 2. Excellent material coverage in lectures and book 3. Take-home exams second exam and final with plenty of time to complete Cons: 1. First exam was stressful and a time crunch 2. Class was way too big for the number of TAs 3. Professor was almost completely absent Detailed Review: The professor honestly may as well not have existed. Maybe there was something he did behind the scenes, but even there it seems like the TA Jeffrey Cochran ran the show. The lectures were 100% just the lectures from when this course was taught by the previous professors, and I doubt anything at all was changed. The TAs were mostly absent except Jeff, without whom this course would have been impossible to complete. He provided just about all clarification for the course and personally answered a truly impressive number of posts given how large this class was. I thought he did a great job. This course seriously suffered due to the size of the class. Grades took a woefully long time to receive, if ever. We never even got a grade for the final, and we didn't receive grades for homework from the first half of the course until the last week of class. I wouldn't say this is a fault of the staff for the course, I think they were just overwhelmed by the number of students. The first exam was stressful with a lot of material covered, pure theory/proofs, and initially closed book and closed notes. The professor did ultimately permit us to use handwritten notes during the exam. The exam was also proctored, which I never appreciate out of principle. The grading was quite generous, however. The second exam was take-home and was mostly implementation based. I think you would have to really drop the ball to not complete it in the time given, which was just over a week. The questions were also formatted such that it was very easy to know if you got the answer right. We were given a whopping 1 month to complete the final, and it was no more difficult than exam 2. Most implementations are in MATLAB and you barely scratch the surface of MATLAB for the assignments/exams. I do not think MATLAB is a difficult language to pick up, especially if you have ever worked with Python (particularly numpy and matplotlib). I would not be worried if you have no experience with MATLAB for this course. This was a good class by and large mostly thanks to the previous professors' efforts and Jeff. I recommend taking this course prior to delving into the ML or AI based courses in the curriculum, if for no other reason than to brush up on your math skills and linear algebra vocabulary and concepts. --- BE WARNED --- The first week was an absolute nightmare. I spent an ungodly amount of time (over 30 hours, easily) on the massive amount of material and homework problems, and was pretty sure I was going to have to drop due to the time commitment along. Unfortunately, you really shouldn't skip any of the homework problems in the book either, because they build on each other and many contained knowledge required for the first exam. However, if you get through this first week, no subsequent week even reaches the 50% mark of time required compared to this first week. Some (several) weeks I spent less then 5 hours on total.

Pros: 1. The importance of linear algebra is just not negligible. 2. 2 take home exams including coding. 3. Cons: 1. Piazza was a nightmare. People ask the same questions over and over again. Other than one, all tutors were basically absent. 2. If you have no solid math background, you will be very likely to struggle to keep up. 3. The course took more students than they could handle. Detailed Review: I took this course and somehow expected it to be challenging, and it turned out to be true. It was quite difficult. You need to do a lot of work to keep up with the assignments and exams. Of course, if you have a solid math background, you should be okay. If not like me, be prepared to spend extra time to keep up. I think the professor was helpful a lot of times, but Piazza was just a nightmare. The tutors were basically absent and cancelled/changed office hours too frequently. There were three exams. One was proof-heavy. The other two were coding + theory. Some of the exam topics were not from the course, so some extra research was necessary. Overall, I do believe that CS students should take this course, despite its apparent difficulty.

Pros: 1. Great detailed online textbook 2. Valuable course for those in program without robust undergraduate linear algebra course experience 3. Cons: 1. Disorganized (long time to receive grades, and ultismrly it appears about 30% of the final grade ended up being free As for everyone because lack of time to grade) 2. 3. Detailed Review: Echoing sentiment from other reviews: this is an extremely manageable course if you have experience with robust mathematics courses from your undergraduate experience. If not, expect a significant amount of effort to keep up. The revival of this course in Spring 2023 was a bit disorganized — one TA (Jeff) really kept the whole thing together by doing awesome work on Piazza Q/A, but the course size was so large that grading and logistics questions often were not responded to timely by course administration.

Pros: 1. I gained a lot of knowledge throughout the semester. 2. The online textbook was easy to understand and follow. 3. The exams were take-home which made it more convenient. Cons: 1. The piazza support was poor, and most TAs did not seem to care about the students' questions or concerns. 2. The grading process was slow, taking more than a month for most assignments. 3. The TAs and instructors seemed disorganized at times. Detailed Review: Unfortunately, the piazza support and TAs were the weakest aspects of the course. While there were five TAs and one professor, only one TA responded to the vast majority of piazza posts. It appeared that many of the TAs did not seem to care in helping the students. Class is overall okay, can learn a lot and not too difficult too follow even if you don't have much linear algebra material.

Pros: 1. Lectures very well presented 2. Readings complement lectures 3. In general, lots of material given to reinforce concepts being taught Cons: 1. Need to fix the grading system 2. Requires a good amount of review from undergrad linear algebra to get going Detailed Review: This class as a whole is very well put together. You do need to be solid with undergraduate linear algebra (the professors that made this course also have a great course specifically for this!) and the first two weeks will feel like a lot of theory thrown at you, but it is groundwork that needs to be laid for understanding/appreciating the analysis in the second part of the course. Our semester was run by a new professor and TAs, and they definitely weren't prepared for the number of people taking the course in an online setting. Grading has taken an extremely long time. I believe grades are an important feedback mechanism for learning, so hopefully the grading process can be smoothed out in future iterations of the course.

Pros: 1. extremely detailed course material from the previous (now retired) professors. 2. while slow, the grading felt fair and thorough, and the exams had very few "surprises" -- everything we were evaluated on felt like a true extension of the textbook 3. eventually, I felt that everything in the course came to a nice crescendo in understanding how many matrix-based math is done under the hood of algorithms Cons: 1. course moves very fast early on, and sometimes leaves you wondering why you are learning certain topics the way you are 2. unclear if this will be consistent in future semesters, but the course staff severely underestimated (and misunderstood) the nature of the course. they intended to grade us with the rigor of a PhD qual evaluation, before ultimately settling on "full points if you tried to solve" the final assignments because they fell so far behind in grading 3. extremely disorganized in submission, grading, communication even amongst the staff Detailed Review: On the course material - their notation style, while unique, helped me to translate things into vectorized approaches in matlab from the lecture notes very easily, and the course book was pretty thorough in explaining proofs (not a lot of "left an as exercise to the reader" in explicitly answered questions) As said in the cons, this might change in the future, but expect to be a little bit on your own in this course. The course staff was unresponsive for weeks at times, and most of the time any response given was by a single TA. The staff was overwhelmed with the volume of grading that needed to be done, and we don't even know our grades on the second midterm as the class comes to an end, let alone the final. The professor would rarely post, and often contradicted other instructions when doing so. It felt a bit like a class that was an afterthought for the staff (except for the one vocal TA that truly was so helpful). I ultimately feel like I did well in this class, and certainly learned a lot, but mostly from self-study of a thorough textbook resource and not so much from the course instructors.

Pros: 1. Well-designed materials especially for the first four weeks. 2. If you thoroughly study all the materials, you'll certainly gain a wealth of knowledge. 3. You can drop the lowest grades for assignment and the questions on edx. For Spring 2023, both Exam 2 and the final exam are as take-home exams. All the TAs are responsitive and work hard to get the things done. Cons: 1. The first four weeks are well-structured with smooth transitions between topics. However, students tend to get lost starting from weeks 7 and 8, as there is a significant gap between the initial weeks and the rest of the semester. The course seems to lack concrete examples and motivation for specific topics, which noticeably increases the learning curve. 2. The new course team appears to have underestimated the workload. We haven't received Exam 2 even though it's the end of the semester. 3. Exam instructions are not self-contained (e.g., there are no links directing you to relevant content), so you need to find some necessary components elsewhere, such as the course GitHub repository. Detailed Review: Once again, I highly recommend building a strong foundation before diving into the semester. The same course team has created an undergraduate Linear Algebra course that you can find on YouTube. It's an excellent resource to help you become familiar with the mathematical concepts and terminology you'll encounter.

Pros: 1. The concepts were pretty well-explained (at least compared to my past experiences of algebra) 2. It seems like a great way to ramp up on algebra for the MSCS. 3. 2nd midterm and final were take home exams and were released over around 2 weeks before the due date. You could almost just think of them as assignments/projects. Cons: 1. Long turnaround on getting grades. We didn't ever get the 2nd midterm marks back, for example. 2. Piazza questions were often not answered or needed students to answer. FWIW, I think there was a very large cohort of students taking the course. They did post a lot of helpful hints & clarifications for the final exam though. 3. Not particularly interesting content (very subjective) 4. Take home exams weren't organized the best. Submission instructions came very close to the deadlines. Detailed Review: The course covers algorithms for computing algebraic things (like eigenvalues/eigenvectors, solutions to matrix equations, etc.). To do this requires understanding a lot of algebra, so this course is probably a great way to get ramped up on algebra. It would also be somewhat helpful for proofs since you see a lot of them, but you don't get enough feedback to improve your skills. I came in with a strong maths background, so this wasn't an issue. The maths foundations from this course should be more than enough to do OLO, QIS, RL, DL, NLP (I haven't done the other courses that require some maths knowledge). The organization was not the best. Take home exams were released early but submission instructions were released very close to the deadline. Homeworks were marked very late (last 2 homeworks weren't even marked; full marks were given if you made a submission).

Pros: 1. TAs are really helpful 2. Learning material is detailed and video are very well made 3. Professors are super nice and quick response for your questions/concerns Cons: 1. Three tests in total 2. Matlab error sometimes confusing 3. Detailed Review If you've had some LA background this class will be easier for you, otherwise it would take you some time to catch up on those LAFF material background knowledge.

Pros: 1. Excellent Professors, and very thorough material 2. Not a common course in Graduate Computer Science, but a very useful one nonetheless 3. Plenty of attention paid to applications, and especially how to modify algorithms for high performance Cons: 1. Unfortunately Maggie and Robert have retired :( 2. The algebraic notation and proofs sometimes impede learning 3. Not directly applicable to many subjects that Computer Science students are most interested in Detailed Review: If you have time, I recommend taking LAFF (available online free) before stepping into this course. Some of the material is pretty basic, but what you really want to expose yourself to is Robert's notation and heuristics. I personally didn't have too much issue with the course but I had taken LAFF, and so I felt like I had a head-start. Took me approx. 9 hrs/week. I took this class with Machine Learning, so both were very math heavy. Decent synthesis, especially in regards to the Singular Value Decomposition. This is definitely a top course in the program, highly recommend that everyone takes it if it is available for you.

Pros: 1. relevant topics 2. accessible lectures 3. Cons: 1. 2. 3. Detailed Review: Great class. Totally worthwhile. Teaches a bunch of advanced topics in the computation of Linear Algebra constructs, like matrix decompositions and eigenvalues. The lectures were very accessible, and the professors were very responsive. However, they are retiring after Spring 2021, so it's unclear if the class will continue. I hope it does, as the material touches on so many other classes in the program, like Optimization and Quantum Computing. The website they put together serves as a sort of textbook, and as such is extremely useful and informative.

Pros: 1. Take-home exams for the second and final exam. 2. Great lectures which explained the theory in-depth. 3. Perfect class for taking in the first semester. Cons: 1. Closed book/notes for first exam. 2. There's a lot of theory to learn and apply. 3. Textbook problems can be difficult to solve. Detailed Review: I took this course for my first semester at MSCS and I feel like this is a great course to take early on. I was taking Machine Learning simultaneously and some of the concepts carried over to that course. This was also a good reintroduction to mathematical concepts after not using them in-depth for a few years. The lectures and textbook are available online and flow together nicely. There was a lot of thought put into how the course was taught. As other reviews have said, the professors really care about this material and want the students to learn it. One issue with the textbook I had was that some of the problems given can be a little difficult. Most of solutions to these problems are given in the textbook with explanations, so this was a minor issue. There were three exams, the first being closed notes/book and the other two being take-home style. I personally don't like closed-note tests since they are typically more memorization than actual problem-solving. I greatly preferred the take-home tests, especially since they were about applying the theory learned in the class to look at the material differently. Assignments were weekly, but there was a regrade system if you wanted to fix any mistakes. Up to two weeks could be used to resubmit any assignments, which was generous. This could also be used if you missed a deadline for the assignment. Some assignments were proofs, others were programming in MATLAB. Difficulty tended to vary by week. Screenshots of the weekly progress on EDX were also required to be submitted. I felt like I never went above 20 hours for both lectures and assignments. But I did need a lot of time for the take-home exams, so I'd recommend putting aside plenty of time during the exam weeks. My main issue with the class was with the material itself. The class is designed so that you generally learn an unoptimized algorithm for solving a problem and throughout the material it builds up into a better optimized algorithm. A lot of theory goes into optimizing the algorithm, which can build up very quickly. By the end of the course, you have a ton of theorems that you need to know how to use. These theorems can be difficult to remember if you aren't keeping track of them. Thankfully, there are summaries at the end of each section which help immensely.

Pros: 1. Notes & videos that are provided are very informative and well written. 2. Weekly assignments are challenging, but reasonable. 3. Take home exams. Cons: 1. The course is very theory based and the applications of the algorithms were not clear. 2. Limited feedback on homework and exams. 3. Difficult to remember the purpose of each of the algorithms that were presented. Detailed Review: Overall, a good a reasonably demanding course. I felt challenged but not overwhelmed. Usually, I was able to spend ~10 hours per week on this course and take the weekends off.

Pros: 1. Well designed/written textbook and videos 2. Good TA support 3. Great for someone with a CS background since there is programming involved Cons: 1. Time-consuming if you decide to work on every problem (including extra HW) and read all the enrichments 2. Workload varies from week-to-week (i.e. not equally distributed) 3. There aren't any other cons I want to cook up because I think this is such a great course! Detailed Review: This is the first course I have taken in the MSCS program after having the distance of 18 years from my B.S. in C.S. I looked at my previous transcript from Georgia Tech and noticed that I had only taken courses in Calculus, Prob. / Statistics, and Applied Combinatorics. No previous linear algebra course! So, the first week 0/1 of this course was incredibly difficult for me. I took the ALAFF pre-test and didn't do so well. So, then I chose to cram a speed-read through the LAFF course only a week before the course started. The first week was hell for me, not having done proofs for so long. If I judged the rest of the course based on that first week, then I would have come to the conclusion that I would not make it to the end. What unfolded after was "exceptional" to me, which is why I titled this review as such. I found Robert and Maggie to be quite welcoming and encouraging -- almost to the extent that I would say they treat us as extended family. I wasn't expecting this through an online course. The material itself is par excellence and weaves in the work that Robert and Maggie have done over the years professionally as researchers. So, although this is primarily a course on theory, the material is grounded in practicality, which I liked. Unfortunately, Robert and Maggie are retiring, so I don't know what the future will be for this course. In the end, I worry that other courses won't live up to this one in the program and my hope is that what I learned sticks! Why? Because I put a hell of a lot of time into it. I took this course alone and would say that I spent 20-30 hours any given week to make sure I covered every homework problem, every "Ponder This", read or at least scanned every enrichment, and even worked the extra homework problems at the end. I wanted to be thorough since I judge this class to be so fundamental to many areas of research.

Pros: 1. Most topics are investigated through example problems to understand how the algorithms are derived. 2. Programming assignments starting near the midway point and beyond are interesting. 3. Consistent approach to problem definition and steps to solve the problems. Cons: 1. 21 hrs/week on average. 2. TA sessions were mostly pointing us to the right areas to re-read in the textbook. 3. No feedback given for homework if you get a good score. Detailed Review: I read someone else's review before taking this course which said it was "straightforward" just as climbing a mountain is "straightforward" but still difficult. I found that was also the case for this course. It required a lot of time to slowly work through every problem, checking the provided solutions, reworking the problem, etc. until I felt comfortable with the solution and the meaning of that solution. I'm 100% sure this is the intended way to go through the course based on what the professors and TAs told us. I tracked every hour and the total averaged to 21 hours/week to go through all the textbook problems and the exams. I spent little time on the enrichment papers and problems due to lack of time. I think it's possible to spend less time if you are skipping problems and going to the second half of every chapter, but you may misunderstand the topic and results. I recommend scheduling out 15-20 hours every week for the entire course and following every problem. That was successful for me. This class would have been impossibly difficult if the online text and videos were not so well prepared. Searching online for additional resources is not that fruitful if you're working on a specific algorithm implementation of a specific factorization or descent method. The beginning programming assignments are a bit too easy but the programming later starting midway through and beyond are actually nice challenges and I found that solving them was a learning experience.

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.

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.

This class was awesome. It's one of the best taught classes in the program. I loved the enthusiasm put by the teachers and the content is of great quality. I found this course not too easy but it was probably due to my background and my college experiences. I surely learnt a lot about proof by induction! If you put the effort needed is probably easy peasy.

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.

This course is super easy!! You just need to do well on the exams, which is very doable. You pretty much can study on an as-needed basis. Exams are open book, so you can basically find the answers on the internet and on the notes. The exam questions are very straightforward too. This is an easy A, please don't miss it. It may seem difficult at first, don't worry about it. Pretty much everybody who took this course on FALL 2020 must have gotten an A even though they found it difficult at first.

This course is "straightforward" but not "easy", in the same way that climbing a mountain is straightforward (just keep moving upwards) but not easy. The course requires quite a lot of effort to really dig deep into the topic. For every week, I required ~15-20 hours of work to complete all the videos, homeworks, and the weekly assignment. It is worth it though; the course content is arranged excellently (I did not once go beyond 20 hours per week, and rarely needed to Google terms). The instructors, Maggie and Robert, are about the nicest professors ever: the deadlines are flexible, and they focus on learning more than submissions and grades. They are masters of their subject (numerical linear algebra) and are very active on edx for doubts. Overall 5/5; one of the best courses I have taken in terms of content and probably THE best course in terms of instructors.

Very well taught course. Professors are nice and responsive and the workload is very manageable. You'll get good practice solving math problems involving vectors/matrices, as well as using MATLAB. Might help to brush up on your undergrad linear algebra beforehand (but not vital) - you can check out their undergrad course, or maybe Gilbert Strang's (MIT) 18.06 course on MIT OCW.

This is legitimately the best-case scenario for me, as someone 1) returning to formal education for the first time in a few years, and 2) who struggled with higher-level linear algebra in undergrad (proofs always stop me in my tracks). The professors genuinely care about learning, and the weekly lessons reflect that. The homeworks interweave with the week's topic very well, and everyone promotes discussion on a public forum in edx. If I ever got stuck, I was not wary of asking for help. They don't necessarily dig as deep as they could in terms of content until the last two or three weeks, so if you are looking for that, this probably isn't the place. This is essentially just a thorough investigation into the most important/vital concepts of Linear Algebra. For those who haven't touched LA in a few years, I would definitely recommend going through their undergrad course that is available on edx ahead of time. That way, you won't have to familiarize yourself with the basics like I did in the beginning. Workload can range from 10 to 20 hours

Among the very best classes I have ever taken. The way the notes, lectures, and homework problems are integrated together really helps in learning the topics. The course has a great narritive structure revisiting and building upon old topics with new material that really helps reinforce the topics. Their ordering of not doing eigenvalues and eigenvectors until the very end was different that what I had previously experienced and what I typically see, but I think it leads to a better understanding of the content. Their method and notation for breaking down the matrices is unique so it may be difficult to find outside materials that directly relate, but I found that the material presented was more than suffecient to learn the topics. The last chapter was a little more haphazard, but COVID-19 stopped them from producing the videos as campus shut down so expect this to be improved in the future. I found the class to be quite easy, but I enjoy linear algebra and have a lot of experience with it. If you need a refresher all their books are available at http://www.ulaff.net/ Workload can range from 6 to 10 hours

Great course. I love the lectures and the course notes. I came to the course with basically zero LA knowledge. It took me a while to catch up on basic LA concepts. I learned a ton from the course. The first exam was a little stressful. All the questions are from class notes. I spent a lot of time understanding everything and there are some memorization required. The other two take-home exams are very well-designed. I learned a lot from taking the take-home exams. The only thing I don't like is that they don't have videos for the last chapter of the course and it's hard to just follow the notes. Although the last chapter is not on the final exam, it is a very important topic. I wish they could make the last chapter better. Workload can range from 8 to 25 hours