Professor
Lara Dolecek
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Most Helpful Review
I did not really like her. She basically wrote the course reader on the board. She has an accent and is hard for to understand. She is not very clear and does not do very many examples. Questions on the hw are impossible and u need to have past solutions. Midterms and finals were fair but her curve was non existent/bad. Did above average on final, average on midterm, 100's on all the hw's, top 10% on the matlab project and i got a B. The TA was pretty whatever, tl dr i would not recommend taking the class with her.
I did not really like her. She basically wrote the course reader on the board. She has an accent and is hard for to understand. She is not very clear and does not do very many examples. Questions on the hw are impossible and u need to have past solutions. Midterms and finals were fair but her curve was non existent/bad. Did above average on final, average on midterm, 100's on all the hw's, top 10% on the matlab project and i got a B. The TA was pretty whatever, tl dr i would not recommend taking the class with her.
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Most Helpful Review
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.
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.
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.
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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Most Helpful Review
Spring 2020 - 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.
Spring 2020 - 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.
Most Helpful Review
Spring 2019 - 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.”
Spring 2019 - 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.”
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