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Grade distributions are collected using data from the UCLA Registrar’s Office.
Grade distributions are collected using data from the UCLA Registrar’s Office.
Grade distributions are collected using data from the UCLA Registrar’s Office.
Grade distributions are collected using data from the UCLA Registrar’s Office.
Grade distributions are collected using data from the UCLA Registrar’s Office.
Grade distributions are collected using data from the UCLA Registrar’s Office.
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2022 spring, I'm currently stats102B with Wu. Wu is not posted as a professor for stats102B, so I'll leave some comments here.
1. Wu's lectures usually composes of the following: 25 mins of review of last lecture, 5 mins of new stuff, and 20 mins of stuttering.
2. First week of lectures, if you thought that you are taking math 115, then you are in the right stats102b class. Wu starts out the first two weeks talking about linear algebra, but he doesn't tell you why.
3. You are expected to master linear algebra. He will do a simple review of it in the first week. You are responsible for knowing every math proof by heart.
4. Wu does not set up any official Q&A forum such as Campuswire or Piazza. So, do not expect any online hw help from professor. He won't reply you in time
5. Wu's lectures don't have the big picture. If you wonder what a big picture is, go checkout Miles Youtube lecture on stats102B. It's public, and first or second class he tells you what the big picture of machine learning/optimization/modeling is.
6. Wu's lecture notes gives no example R code, but all homework is in R.
Terrible. Just terrible. Gives a lot of arbitrary extraneous specifications for the homework that are either easy to miss or incredibly unclear such that if you fail to meet those specifications (and it's all to easy to do so), you'll get heavily penalized. In addition, grading for the homework was screwed up and a regrade had to be done, but even the regrade was screwed up. Incredibly unresponsive to emails. Goes through the most difficult topic of the syllabus two days before the exam, and sets up over half the questions in the exam to test that topic. I have never ever hated a module more in my whole academic life. I legitimately got anxiety from this mod. Do yourself a favour and skip this module/prof if you value your sanity and mental health.
Exams and quizzes were generally not easy. You need to watch every one of the discussion videos and to browse campuswire twice a day in order to finally get the details for your assignments clear. (what is desired is often not well specified in the spec) Despite all these, the course has really generous curve.
The midterm and final were extremely hard. The average score for the final was a 32%. The tests seemed like they were designed to trick you and the problems were many steps and took a very long time. Nothing on the test was really conceptual and endless studying of the lecture slides were not enough because the questions are not big picture at all and rather very niche and expand on edge cases or details that were either never or hardly covered. There was no practice test and the test guide was very vague and often did not include material that was on the test. The homeworks, tests, and lecture examples were all extremely different so the learning of material was all over the place and never really solidified. The homework were very challenging although the TA pretty much went through every problem during discussion which were recorded. His coding was often incorrect however and it seemed like he didn't prep very well before the discussion. Overall, I would advise to take this class with another professor but I will say the curve is fair and the professor is very accommodating.
The curve on this class was absolutely crazy so there's that. Midterm and final class averages were extremely low but the curve, homework assignments and chapter quizzes help. Guani is a sweet guy and ok lecturer - he deviates from his slide deck in the second half of the class but you can still use it to study. TAs were super helpful with the homework.
I never leave reviews for professors because I rarely feel a strong connection with them, but Professor Wu was different. I struggled with attending class regularly, but never his, even though it was at 9:30 AM. His lectures are incredibly engaging, and our class felt comfortable asking questions out loud while he taught. He never made anyone feel stupid, no matter the question. On top of that, he’s a generous grader and curves fairly.
Assignments and exams are designed to trick you instead of help you learn. Seriously, I spent so much time debugging his stupid PQ numbers instead of learning object-oriented programming.
Extremely unengaging lectures, very uncommunicative and very unclear. Homework assignments are a guessing game of what he actually wants you to do because the instructions are extremely vague and unclear. Tests consist of multiple choice questions of randomly nitpicked details from lectures instead of actually testing your ability to code in R. Class averages were very low on tests. Would not take again.
2022 spring, I'm currently stats102B with Wu. Wu is not posted as a professor for stats102B, so I'll leave some comments here.
1. Wu's lectures usually composes of the following: 25 mins of review of last lecture, 5 mins of new stuff, and 20 mins of stuttering.
2. First week of lectures, if you thought that you are taking math 115, then you are in the right stats102b class. Wu starts out the first two weeks talking about linear algebra, but he doesn't tell you why.
3. You are expected to master linear algebra. He will do a simple review of it in the first week. You are responsible for knowing every math proof by heart.
4. Wu does not set up any official Q&A forum such as Campuswire or Piazza. So, do not expect any online hw help from professor. He won't reply you in time
5. Wu's lectures don't have the big picture. If you wonder what a big picture is, go checkout Miles Youtube lecture on stats102B. It's public, and first or second class he tells you what the big picture of machine learning/optimization/modeling is.
6. Wu's lecture notes gives no example R code, but all homework is in R.
Terrible. Just terrible. Gives a lot of arbitrary extraneous specifications for the homework that are either easy to miss or incredibly unclear such that if you fail to meet those specifications (and it's all to easy to do so), you'll get heavily penalized. In addition, grading for the homework was screwed up and a regrade had to be done, but even the regrade was screwed up. Incredibly unresponsive to emails. Goes through the most difficult topic of the syllabus two days before the exam, and sets up over half the questions in the exam to test that topic. I have never ever hated a module more in my whole academic life. I legitimately got anxiety from this mod. Do yourself a favour and skip this module/prof if you value your sanity and mental health.
Exams and quizzes were generally not easy. You need to watch every one of the discussion videos and to browse campuswire twice a day in order to finally get the details for your assignments clear. (what is desired is often not well specified in the spec) Despite all these, the course has really generous curve.
The midterm and final were extremely hard. The average score for the final was a 32%. The tests seemed like they were designed to trick you and the problems were many steps and took a very long time. Nothing on the test was really conceptual and endless studying of the lecture slides were not enough because the questions are not big picture at all and rather very niche and expand on edge cases or details that were either never or hardly covered. There was no practice test and the test guide was very vague and often did not include material that was on the test. The homeworks, tests, and lecture examples were all extremely different so the learning of material was all over the place and never really solidified. The homework were very challenging although the TA pretty much went through every problem during discussion which were recorded. His coding was often incorrect however and it seemed like he didn't prep very well before the discussion. Overall, I would advise to take this class with another professor but I will say the curve is fair and the professor is very accommodating.
The curve on this class was absolutely crazy so there's that. Midterm and final class averages were extremely low but the curve, homework assignments and chapter quizzes help. Guani is a sweet guy and ok lecturer - he deviates from his slide deck in the second half of the class but you can still use it to study. TAs were super helpful with the homework.
I never leave reviews for professors because I rarely feel a strong connection with them, but Professor Wu was different. I struggled with attending class regularly, but never his, even though it was at 9:30 AM. His lectures are incredibly engaging, and our class felt comfortable asking questions out loud while he taught. He never made anyone feel stupid, no matter the question. On top of that, he’s a generous grader and curves fairly.
Assignments and exams are designed to trick you instead of help you learn. Seriously, I spent so much time debugging his stupid PQ numbers instead of learning object-oriented programming.
Extremely unengaging lectures, very uncommunicative and very unclear. Homework assignments are a guessing game of what he actually wants you to do because the instructions are extremely vague and unclear. Tests consist of multiple choice questions of randomly nitpicked details from lectures instead of actually testing your ability to code in R. Class averages were very low on tests. Would not take again.
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