The prof sucks. I liked the class material, but the prof made me not like it. She never responds to any emails. She only teaches the minimum, barely any examples or anything close to the tests. She refused to give us practice exams, telling us to just see the book. The TA carried the class, he was great. The prof was very bad. The math department sucks in consistency of quality of professors as well as grading. I think she said she wouldnt curve, even though the average on the midterm and final was C- (71~72%).
She purposely isnt allowing regrade requests for the final for some reason, her grading sucks (since I've found multiple mistakes).
If bruinwalk would allow me to have helpfulness and clarity as 0, I'd give 0

I was pretty shocked by the low reviews. Prof. Kassab is a very fair and kind instructor. Some may complain that the tests were "proof-heavy", sure, but it's not like these proofs were hard at all. There were really only 6-8 of them that you needed to know, and she went over every single one in class (or left it as an exercise that can be found in the book). Really - not bad at all!
She gave us a practice midterm which I found helpful. I didn't do too well on the midterm because it was rushed and I made some arithmetic mistakes. (Unfortunately, the exam rubric punishes arithmetic mistakes heavily, so be very careful in your calculations!) However, she has a fantastic, forgiving grading scheme that allows the midterm to be replaced by the final. During the final, there is wayyy more time than the midterm so you can do better.
Another thing I found really helpful was that she gave us a list of final study topics. I don't know if it was just because we were online for 1.5 weeks this quarter, but she ended up cutting out a lot of content in the final, which made things super clear. (I think our final was lighter on content than the other 164 class.) She also gave an extra credit opportunity on the last homework! Pretty sweet. But, once again, idk if she only offered this to us because we were partially online.
Finally, one more piece of advice: whenever Prof. Kassab recommends a proof, DO IT.
Overall, Prof. Kassab is helpful and wants her students to succeed. It is really clear she cares about her students. She would even show us coding examples of some of the algos, which I thought was pretty cool. I am really glad I took this class.

I attended pretty much every single lectures because she doesn't post any notes or slides, just recommends you to read the textbook. Her notes from class did end up being pretty helpful for the homework and studying later on, so it was worth attending. There was 5 homework assignments for the whole quarter, though I am not sure if she reduced the amount due to us going online for some time. Overall, she was a very nice professor and was always asking the class how we were doing and what she could do to help us (especially when the protests started happening). I failed the midterm, which was my fault because I didn't study enough, the class average was an 83% for it. But thankfully the second grading scheme saved me, and I got an 80% on the final, which was graded very generously. Overall, I think if you just show up to class and do the homework to study, you'll be okay.

My third time having Kassab and since I had her for math 118 and 164, this was the second class I've taken at UCLA where i never attended lecture or discussion (first time being my third Miles Chen class lol his youtube vids are the best). Here's my pro-con breakdown after finishing the final an hour ago and not having received my grade yet:

Pros:
- lecture slides are just watered down textbook notes. she leaves some slides blank where she does stuff like MLE by hand, but the derivations are all in the textbook and really not that hard if you've taken stats100b.
- everything is chatgpt-able. HWs, blank slides in lecture notes, even the project is chatgpt-able. The material isn't necessarily simple, but it's basic ML that's very well documented or basic linear algebra hence GPT-4 can solve pretty much every problem in the course. I also used chatgpt to help me study by explaining concepts, especially since i never went to class lol.
- exams are very conceptual and not too mathy. It's technically a math class, but realistically you'll never need to perform linear regression or SVM or train neural networks by hand irl. what's useful irl is understanding how everything works so you know when/where to use different techniques. Exams reflect this philosophy as the math problems are fairly easy to solve and the rest are conceptual.
- exam topic list. a week before the final, she sent out a list of things to study, things not to study, and things you wouldn't have to memorize. She didn't do this for the other classes i took of hers, so she might've just been feeling generous lol.

cons:
- a lot of kids in my TA's discord complained about how the exams covered so little of the material and they were too conceptual. If you're the type of person who does a lot of practice problems and derivations, the conceptual elements of her exams might be annoying. I recommend truly understanding the concepts (like watch a visualization on YouTube or something) before doing any practice problems.
- lectures are useless. i always felt like her lectures were just a waste of time. i tested my hypothesis by not going to a single class and cramming all the materials from after the midterm to the final in 3 days (excluding topics she specified weren't going to be on the final) by reading thru her lecture slides and previous hws. I finished the final within 2 hours and think i did pretty well (def passed) hence i believe this is a viable strategy for those afflicted with senioritis.

EDIT: Got an A-, not too shabby for a chronic absentee

It was her first quarter teaching at UCLA (i believe) and her teaching is pretty mid. I noticed her lecture notes are just watered down material from the textbook and you could easily get by with skipping lectures, doing hw, and reading the textbook or something. Stopped going to lectures around week 2 and never went to discussion and passed, if you have senioritis like me then you'll love her lol

Math 135 itself is ought to be a straightforward class. Kassab made it unnecessary obscure. Midterm is relatively fair but the final is hard. No practice exams or review sessions for the tests. Would not recommend.

Genuinely awful class. Just made me miserable and felt like a complete waste of my time. Horrible, unclear lectures delivered with a really depressing tone. The content of the class sucked too -- lacked cohesiveness or direction. Would have really, really benefited from a final project or ANY kind of application. Absolutely no preparation or direction for exams. She wouldn't even tell us how many questions were on it. It's not even that it's super hard -- it's just that you're given no motivation or direction to apply yourself, or any direction on where to spend your time studying. At least a tough class like 131a has a strong foundation and reason for being difficult. This one is just ridiculous. Horrible, don't take Kassab unless you have absolutely no choice. Awful.

We needed to use practice tests from other classes because she refused to give practice problems. She wouldn't even give us solutions to the HWs and tests. She made it very difficult to learn the material. Even though a class might be hard, professors will try to help accommodate students to help them learn, but it felt like she didn't want to help us out.

pretty useless class and hard, not easy to get an a. the concepts are not interesting either.

If you are motivated by machine learning, I highly suggest taking this class with professor Kassab. I found her willingness to answer questions extremely helpful. She acknowledges the rapidly evolving nature of machine learning, as well as the textbook's dense approach, so lectures offer a chance to cover additional context, such as building the intuition behind theories, calculations omitted from the textbook, (good) hand drawn visuals, examples of modern applications, connections to recent advancements, best practices, and guiding heuristics. She carries a wealth of information and is clearly passionate about the field, so her lectures always ended up feeling "too short." I certainly feel that I got my money's worth out of this class, and I highly disagree with the previous reviewer's perspective. They are a great example of how cheating is only cheating yourself. For the intrigued, these lectures will provide a tremendous opportunity for you to build a solid foundation in machine learning.