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.

Damn this class was tough. Attending lectures is a must since the assigned book in my experience was too complicated with its mathematical notation of simple concepts. The midterm and final exams were also quite tough with the midterm average at around 67. However, I do give Professor Dolecek props because her lectures were clear and well paced for the amount of material she taught.
Also there is a final project on matlab that spills over into finals week so figure out how to schedule your time with working on it.

Despite my grade, I would rank this as one of the hardest classes I've taken. Professor Doleček does a very good job as a lecturer (probably because of her experience teaching this class). The homework was challenging but didactic, and I truly felt I learned a lot from the class.

However, the midterm was just brutal. Walking out of the midterm I felt like I utterly failed – even worse than any test in a Professor Paul Eggert class, for those of you CS/E majors reading this. But they do give out significant partial credit, so you get a lot of points just for being on the right track. The final was hard but less so compared to the midterm. The curve was pretty generous though.

She doesn't upload any lectures or slides on CCLE, but she writes down everything on board, every concept from basic to advance, every proof even with the baby steps. She's really good at teaching and her lectures were amazing, were after taking her class I started to do a minor in math as well. Her class is based on weekly hws that involves matlab coding, a project at the end which she gives you at least 3-4 weeks to do it, and a midterm and final which is really similar to her leture examples not the hws.

This class quite challenging.Be prepared for a lot of math (matrix calculus, probability, convex optimization). Professor Dolecek goes through all the mathematical proofs in detail. But you still need to spend some extra time to learn and understand them. (Also her handwriting makes her slides unreadable, so do take notes during lectures otherwise you won't understand what she was writing).
The homework is usually 5-6 problems, with 1 or 2 asking you to implement an algorithm you learned in class with python or matlab. It is definitely time consuming, especially the programming problems. I personally spent ~10hrs per week for the homework (partially because I'm not familiar with python and matlab). However, after struggling through all the problems, I did learn a lot.
The midterm was a lot easier than the homework. So don't get too stressed for that. (I can't speak for the final though bc I opt-out of it).
Overall I'll recommend this course. It's challenging but you learn a lot.

Since I've already taken Math 170 sequence courses, I walked into this class without expecting to learn much. However, this was not the case. I thought I've already learned probability extremely well, but Professor Dolecek's teaching truly made my understanding clearer than ever. One of the highlights of this course was definitely the coding final project since we were able to simulate probabilistic scenarios with computation, and I had tons of fun with it. The professor was also pretty accommodating by creating an additional midterm to alleviate the pressure of depending on a single midterm. Overall, I would highly recommend taking this course with Professor Dolecek.

Overall this is a math class, with minimal programming questions sprinkled in. Midterms, final, lectures, and 75% of the hw questions were all math proofs/questions. However, the exams were pretty easy and straightforward. They were open book, which was a lifesaver since the questions were heavily based on her lectures. Recommend taking 10/10

Overall, I'm very happy with this class and its instructors (Lara Dolecek, Lev Tauz, Mitra Debarnab). I come from a CS background and have taken 1-2 machine learning classes (a few years ago) where they never really delved into the mathematical details of distributions, their origins and properties. This class helped fill that gap for me and I feel more confident in my understanding of the maths behind ML.

As for the class itself, it covers pretty much everything from the initial axioms that define probability up to common statistical measures such as covariance, squared error and correlation. I took this class remotely via the MSOL program and had a grade breakdown as follows:
-25% Homework (8 in total)
-25% Midterm (open book)
-25% Assignment (solo)
-25% Final (open book)

10 weeks is not a lot of time for this and it shows: the class moves at a fast pace, particularly towards the end. Even in the week of the final, a sizable number of new topics were introduced (fortunately, they did not feature in the exam). I personally did not do as well in the final exam, but got good grades otherwise, ending up with 88% overall.

Some tips for new students:
-The following concepts are useful to know: set theory, multivariable calculus (partial derivatives, double integrals, limits, convergence), convolution, Fourier transform, sums and sequences, complex numbers, gamma function and delta function. You don't need to know all of them, but most should be familiar.
-The textbook is your friend: it covers the content of the lectures at a higher level of detail and has useful examples when you're struggling with the homework.
-The discussions are helpful, as they will go over more advanced problems that the lectures do not address. Also, they are usually a bit more difficult than the exam, so if you take the time to solve and understand those you should be fine.
-The assignment is an easy way to boost your grades as the concepts are not particularly difficult. However, it does take quite a while to write up the MATLAB programs and report, so make sure to start it before the last week so you have some idea of how long it will take you.

If you're a CS major (or basically any major not required to take 131A) don't take it imo. I would have taken Math170E or Stats100A in hindsight. The topics are interesting and presented well, but the TAs this year sucked at making exams. We had a pretty wide distribution for the midterm (70 median with 1 std dev getting you to a 90). Homeworks were pretty easy outside of some really dumb questions. Towards the end it became all calculus and no probability. Also, the final exam was terrible this winter. The TAs who wrote the exam changed one of the PDFs for a question worth 1/6 of the final exam about halfway through. There were too many questions also. A couple parts of some questions were just wacky algebra that we've never seen on discussion, homework, or in-class. It was a top 2 worst final exam experience and it's not #2.

This class is fine. The exams weren't too hard, the homework load was fine. Homework was so light at the beginning and then became very time consuming at the end. The class for me was easy in the beginning(with combinatorics and stuff) but at the end it got much more complicated. The lectures are good and the professor aims to connect the material to real life applications with example problems which is good. Handwriting on the notes is sometimes hard to read, but just ask the professor if you don't know what she wrote and she'll tell you. The project is fine, but try to start on it early and ask lots of questions about instructions if you are unsure.