AD
Based on 5 Users
TOP TAGS
- Uses Slides
- Tolerates Tardiness
- Needs Textbook
- Useful Textbooks
- Often Funny
- Gives Extra Credit
- Has Group Projects
Grade distributions are collected using data from the UCLA Registrar’s Office.
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AD
Zes is pretty nice, but his lectures aren't very in depth; they basically just skim over the corresponding textbook chapters without explaining much (they're good as big picture overviews of the material, so I'd recommend reading the textbook chapters before coming to lecture).
He makes an effort to get to know his students (learning all of our names) and is quite helpful during office hours.
Homework is book problems, which generally aren't bad.
Midterm is open note and open book; as a reward for going to the lecture before the midterm, he actually showed us 2 of the questions on the exam (along with the answer). There is no final; there is a kaggle competition instead.
Lectures are a waste of time. Zes digresses all the time so the frequency of useful information is very low. You don’t need to listen to lectures for assignments or tests. Honestly this class is a shallow overview of the methods that a lot of us already knew and to apply those methods, you use the R functions out there without thinking about the math behind those functions. This maybe a mean statement to say but I question the qualification of Zes as a lecturer and his ability to teach properly.
Class was a cakewalk, mostly because he followed our textbook (ISLR) pretty much exactly, and the textbook is very well written - it's mostly practical/conceptual, with not as much emphasis on mathematical rigour. He's a funny and approachable guy, but I didn't bother going to lectures. The few that I did go to focused on concepts, just like the book. If you put in the time to complete the homework assignments, then you should do well. Final project was a kaggle competition, and final exam was all multiple choice conceptual questions. The hardest part of the course was just putting in the time to do the homework and do the reading.
Dave is really helpful and puts an effort into getting to know his students. However, if you come to this class with prior knowledge of data mining (e.g. STATS 102B, research or internship ML-experience), you will likely find this class not in-depth at all. My friends and I agreed that while the class was fun, we didn't end up learning a lot that we didn't know already.
Zes is pretty nice, but his lectures aren't very in depth; they basically just skim over the corresponding textbook chapters without explaining much (they're good as big picture overviews of the material, so I'd recommend reading the textbook chapters before coming to lecture).
He makes an effort to get to know his students (learning all of our names) and is quite helpful during office hours.
Homework is book problems, which generally aren't bad.
Midterm is open note and open book; as a reward for going to the lecture before the midterm, he actually showed us 2 of the questions on the exam (along with the answer). There is no final; there is a kaggle competition instead.
Lectures are a waste of time. Zes digresses all the time so the frequency of useful information is very low. You don’t need to listen to lectures for assignments or tests. Honestly this class is a shallow overview of the methods that a lot of us already knew and to apply those methods, you use the R functions out there without thinking about the math behind those functions. This maybe a mean statement to say but I question the qualification of Zes as a lecturer and his ability to teach properly.
Class was a cakewalk, mostly because he followed our textbook (ISLR) pretty much exactly, and the textbook is very well written - it's mostly practical/conceptual, with not as much emphasis on mathematical rigour. He's a funny and approachable guy, but I didn't bother going to lectures. The few that I did go to focused on concepts, just like the book. If you put in the time to complete the homework assignments, then you should do well. Final project was a kaggle competition, and final exam was all multiple choice conceptual questions. The hardest part of the course was just putting in the time to do the homework and do the reading.
Dave is really helpful and puts an effort into getting to know his students. However, if you come to this class with prior knowledge of data mining (e.g. STATS 102B, research or internship ML-experience), you will likely find this class not in-depth at all. My friends and I agreed that while the class was fun, we didn't end up learning a lot that we didn't know already.
Based on 5 Users
TOP TAGS
- Uses Slides (2)
- Tolerates Tardiness (1)
- Needs Textbook (2)
- Useful Textbooks (2)
- Often Funny (2)
- Gives Extra Credit (1)
- Has Group Projects (2)