EC ENGR 131A

Probability and Statistics

Description: Lecture, four hours; discussion, one hour; outside study, 10 hours. Requisites: course 102 (enforced), Mathematics 32B, 33B. Introduction to basic concepts of probability, including random variables and vectors, distributions and densities, moments, characteristic functions, and limit theorems. Applications to communication, control, and signal processing. Introduction to computer simulation and generation of random events. Letter grading.

Units: 4.0
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Overall Rating 4.5
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Most Helpful Review
Winter 2020 - 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.
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