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Preface: the quarter I took this class, UCLA was affected by the COVID-19 pandemic, so the grades and class structure were probably skewed.
I've found that EE classes at UCLA tend to be extremely brutal, but this is one of the better ones. In no way is this class easy, it's just that while it's brutal, you actually learn the material extremely well, and Professor Dolecek has a good teaching style (at least for me personally). Honestly, for EC ENGR 131A, she's probably the best professor you're going to get. Sure you'll have hard exams but for the most part they're fair and she's a nice person who genuinely cares that students are learning (and helpful in office hours).
Originally, there were 8 scheduled problem sets, a final project [involving MATLAB], a midterm (only 1), and a final (due to COVID-19, the final exam was made optional however). Here is the original grading breakdown and then the modified one (if you opted out of the final):
15% - Homework
10% - MATLAB Project
30% - Midterm
45% - Final (optional for our quarter)
If you chose to opt out of the final exam, your grade was determined solely based off of the other factors. Lectures and discussions have a certain structure and pattern, which I found to be extremely consistent and conducive to student learning. For a 2 hour lecture, we had a 10 minute break at the 50 minute mark, and lectures always started off with an outline of today's new topics and a recap of last lecture. She follows all her theory with worked examples, and doesn't skip steps in the proofs, which is a plus. While she's slightly more on the theoretical side of teaching, for a course on probability and statistics, I have no qualms about that. Discussion sections were useful to me, as we reviewed the week's new material and practiced additional problems on reinforcing concepts. My TA (Lev Tauz), was really good at throwing some of his own questions to get us to think, and was very sociable.
One thing I must remark upon is the difficulty of this class. Leading up to the midterm, content and homework was very bearable, but afterwards they decided to ramp it up a notch. In particular, the last two homeworks took up a lot of time, and I felt they were a little unnecessarily long (and maybe slightly sadistic lmao). For the MATLAB project, make sure to start early (they assign it ~week 7 and it's due finals week), so that you can ask your questions early and get answers on how to do it, as opposed to starting it week 10 and spending the weekend before finals week trying to complete it. Midterm average was ~87%, which Professor Dolecek seemed pretty happy about, but don't be fooled: for previous years and most of the quarters she's taught this course, the exams are notoriously difficult and have much lower averages.
Overall, I definitely felt like I learned a lot throughout this course. You'll start off basic with set theory, transition to random variables, and then unify this with some of the higher principles of probability (e.g. Law of Large Numbers, Central Limit Theorem, etc.). While it's a difficult course, I promise you that if you stick with it, you'll feel extremely satisfied seeing your work come off, or being able to get the correct display for the MATLAB project, as it really makes you work for it but I guarantee you'll feel proud at the end of the day if you persevere. Definitely would take again for this course.
Note: Class taken remotely during COVID-19.
Professor Dolecek is a truly accommodating professor and I think everyone in the class can tell that she truly cares for her students. Right from the very first day, she had established plans on how we will take the midterm and final which were both 24hrs open book open notes. It was nice to know what we were getting into.
Her lectures were clear most of the time and they were all through pre-recorded videos. It was nice that we could go at them at our own pace. If we had question, there was a forum where we can ask them and both the professor and the TAs answered in a timely manner. I felt like I was able to learn a lot from her class, and the hws really help reinforced concepts taught in class. It is important to note that her class is VERY MATH(linear algebra)-HEAVY which was kinda expected given ML is heavily based on math. A lot of the exam questions and homework questions are math proofs, and every question involved either linear algebra, multivariate gaussian, or multivariate differentiations in some form. You will come out of this class having not only understand basic ML concepts but also how all the math works behind the scene.
Her HWs are difficult but doable. Once you do them, you can be sure that you understand the lecture materials and will be well prepared for her assessments.
I strongly suggest taking M146 with Professor Dolecek as you will for sure learn a lot from her and have a decent experience :)
She's the best teacher for probability. Ever. She went through proofs in class that were crucial to intuition, and she didn't go too slow or too fast. She taught everything beautifully, and I thought the midterms were the most fun tests I have ever taken. She knows her stuff too, considering her background. If ever someone can teach probability, it would be Lara Dolecek.
Prof. Dolecek is a good person, and she is very knowledgeable when it comes to the course material. I want to make this clear that she is NOT a bad person or mean or anything.
That being said, there are some points that you should know if you were to choose her lecture (especially for remote learning):
1. She didn't use zoom. All lectures are pre-recorded and posted on CCLE for my quarter.
2. She has terrible, terrible handwriting. Sometimes you cannot tell subtractions apart from multiplications (she writes · and - really casually), also from time to time her writing becomes unreadable and you have to rely fully on listening.
3. For some reason, in the middle of the quarter she switched from ball-point to highlighter to write on her slides, just when you think her handwriting cannot get any worse...
So pretty much her handwriting has made this course harder than it should be, and the highlighter is plain suffer for remote learning. But again, Prof. Dolecek is a good person, she would answer questions and can explain stuff for you when you are stuck.
I took this class during the pandemic and everything was taught online so my experience might be different than a regular semester. Having said that, she is probably the worst CS professor I have ever got.
First of all, her class is basically a math class rather than an ML class. I get that math is important and is the core of ML, but it's not the ONLY thing. All her lectures are just her writing math (in horrible handwriting that you probably won't understand) and talking about math. She doesn't care about explaining the algorithm or give the intuition behind why and where we would use the particular methods. She just cares about putting down the math and proving shit that you probably don't need. I literally had to watch Andrew Ng's lecture on SVM that he taught in a GRADUATE class at Stanford to understand what the hell the professor was talking about. The math would probably not be daunting if she just knew how to teach. If you want to pass her class then you need to be a math genius and follow another ML course alongside to get an A in the class.
Also, homework is basically the same thing. It's all about math and proving this proving that. There's a coding portion to the homework but those questions test your coding abilities rather than any ML knowledge. Most of the homeworks contained questions where you had to use a library that is practically impossible to figure out how to use. And the worst part is nor the professor or the TAs talk about it.
However, her tests were doable and we were given 24 hours to finish them. So in the end I managed to get an A only because of online schooling and take-home exams.
This was the first time Professor Doleček taught Machine Learning. Having taken Electrical and Computer Engineering 131A (Probability) with Professor Doleček, this class was a minor disappointment. Especially near the beginning of the class, the lectures were fairly unclear – to this day, I don't have the strongest grasp of Bayesian statistical terms (prior, posterior, likelihood) that the student is expected to know for the rest of the class. However, her teaching settled down a bit after a few weeks, but it somehow never quite seemed to reach the clarity of her 131A lectures.
Compared to 131A, this class was around the same difficulty level. The homework had a lot of strenuous calculus in it, but you do learn a lot if you were to put in the effort to do them. (Apparently the TAs explain them in some level of detail, but I found it difficult to understand them so chose not to go to discussions most of the time. They did post notes though, which I didn't find out till week 7 or so. Oops.)
On the other hand, the exams were a few orders of magnitude easier. Perhaps it's just because it was the first time Professor Doleček taught this class, but the exams were pretty much the same things as homework problems, with some conceptual questions mixed in.
Also check out my review for course 131A: https://bruinwalk.com/professors/lara-dolecek/ec-engr-131a/, and search for “one of the hardest classes.”
Lots of material is covered in this class. Lara knows her stuff but can be very intimidating sometimes. Final was ridiculously difficult.
131 is a hard class in general (unless you're a math major taking this for fun, in which case it was probably child's play), but Lara is a good professor.
Lots of content. Workload is not too bad, a couple problems a week and a MATLAB project towards the end. Lectures were definitely worth the two hours. Midterm and final have grade distributions are low but curved. Would take this class again with this professor.
The exam is very tricky. It seems the professor want to lower the grade as much as possible. Grading is harsh. You will lose a lot of points on the stuff that you know how to do. Probability is not an easy class, but this professor make it harder. Her lecture is okay, but her hand-writing is extremely difficult to decipher.