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- Miles Satori Chen
- STATS 102B
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Based on 7 Users
TOP TAGS
- Uses Slides
- Needs Textbook
- Engaging Lectures
- Would Take Again
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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Great class. Good professor, though slightly overrated in my opinion (but I'd definitely take him again lol). Odd tests.
This is basically a ML class (less intensive maybe as CS). He uses his slides and explains well. He records it too.
We learn the standard algos like KNN, K means, Neural nets, EM algorithm, Bayes classifier, SVM, PCA and their math behind it. He teaches the concepts well and makes it extremely concise (dims it down to make it simple to understand, maybe too simple).
His homeworks are heavily weighted, so make sure to finish them well. 6 of them, each being 6%. The view quiz this quarter changed such that he'd provide the last one after the recording stopped to incentivize people to come in person.
The tests are pretty weird. They are easy and seem like high school style. The thing is he doesn't give much partial credit at all. And since the style is like high school, some questions about machine learning and long math calculations are all for a fill-in-the-blank. And so even with your work, you can end up getting 0. So double check your work.
Miles is great! You learn how to use and implement a lot of machine learning algorithms.
Grading is as follows:
6 HW's each worth 6%: 36%
2 Midterms each worth 15%: 30%
Attendance Quiz (can watch video too): 10%
Campuswire 4%
Final: 20%
The HWs are not too difficult, as he provides example code that is similar to HW assignments for most of the problems.
The midterms and final are relatively easy, with the conceptual questions being the hardest part, though you can completely bomb that part of them and still easily get an A.
Overall, a great class.
After taking STATS 102A with Professor Chen, I was excited to have him again for STATS 102B, and he did not disappoint. While STATS 102A had a heavy coding emphasis, this class focused more on algorithms and the math behind said algorithms. For the midterms--particularly the second one--I was pressed for time, but that was the only uncomfortable part of the course. Having taking STATS 101C prior to this class, it was an enjoyable experience; 101C and 102B have quite a bit of overlap. In fact, I would term 102B as "coding 101C," which I enjoyed because it allowed me to better understand how these algorithms actually work rather than simply filling out a template with defaults. STATS 102B is one of the more important classes in the stats major, and Professor Chen made it a great experience.
Participation on Campuswire is crucial so that you get full credit on that part of your grade. No homeworks are dropped, but they aren't too difficult either. Exams demand speed and thus decent familiarity with the material. Make good use of his study guides and thoroughly review his slides. He does not curve, so do study hard.
Tip: You might want to take notes during lectures, especially on discussions/explanations that are not on slides. This may help you streamline exams even faster.
Professor Chen is extremely caring and kind. He's willing to share his views on career planning, grad school and life philosophy. He loves teaching and explains stuffs well. His lecture slides are logically organized, easy to read and informative. Great instructor and awesome person.
Great class. Good professor, though slightly overrated in my opinion (but I'd definitely take him again lol). Odd tests.
This is basically a ML class (less intensive maybe as CS). He uses his slides and explains well. He records it too.
We learn the standard algos like KNN, K means, Neural nets, EM algorithm, Bayes classifier, SVM, PCA and their math behind it. He teaches the concepts well and makes it extremely concise (dims it down to make it simple to understand, maybe too simple).
His homeworks are heavily weighted, so make sure to finish them well. 6 of them, each being 6%. The view quiz this quarter changed such that he'd provide the last one after the recording stopped to incentivize people to come in person.
The tests are pretty weird. They are easy and seem like high school style. The thing is he doesn't give much partial credit at all. And since the style is like high school, some questions about machine learning and long math calculations are all for a fill-in-the-blank. And so even with your work, you can end up getting 0. So double check your work.
Miles is great! You learn how to use and implement a lot of machine learning algorithms.
Grading is as follows:
6 HW's each worth 6%: 36%
2 Midterms each worth 15%: 30%
Attendance Quiz (can watch video too): 10%
Campuswire 4%
Final: 20%
The HWs are not too difficult, as he provides example code that is similar to HW assignments for most of the problems.
The midterms and final are relatively easy, with the conceptual questions being the hardest part, though you can completely bomb that part of them and still easily get an A.
Overall, a great class.
After taking STATS 102A with Professor Chen, I was excited to have him again for STATS 102B, and he did not disappoint. While STATS 102A had a heavy coding emphasis, this class focused more on algorithms and the math behind said algorithms. For the midterms--particularly the second one--I was pressed for time, but that was the only uncomfortable part of the course. Having taking STATS 101C prior to this class, it was an enjoyable experience; 101C and 102B have quite a bit of overlap. In fact, I would term 102B as "coding 101C," which I enjoyed because it allowed me to better understand how these algorithms actually work rather than simply filling out a template with defaults. STATS 102B is one of the more important classes in the stats major, and Professor Chen made it a great experience.
Participation on Campuswire is crucial so that you get full credit on that part of your grade. No homeworks are dropped, but they aren't too difficult either. Exams demand speed and thus decent familiarity with the material. Make good use of his study guides and thoroughly review his slides. He does not curve, so do study hard.
Tip: You might want to take notes during lectures, especially on discussions/explanations that are not on slides. This may help you streamline exams even faster.
Professor Chen is extremely caring and kind. He's willing to share his views on career planning, grad school and life philosophy. He loves teaching and explains stuffs well. His lecture slides are logically organized, easy to read and informative. Great instructor and awesome person.
Based on 7 Users
TOP TAGS
- Uses Slides (4)
- Needs Textbook (4)
- Engaging Lectures (4)
- Would Take Again (4)