Ying Nian Wu
Department of Statistics
AD
5.0
Overall Rating
Based on 1 User
Easiness 3.0 / 5 How easy the class is, 1 being extremely difficult and 5 being easy peasy.
Clarity 5.0 / 5 How clear the class is, 1 being extremely unclear and 5 being very clear.
Workload 5.0 / 5 How much workload the class is, 1 being extremely heavy and 5 being extremely light.
Helpfulness 5.0 / 5 How helpful the class is, 1 being not helpful at all and 5 being extremely helpful.

TOP TAGS

There are no relevant tags for this professor yet.

GRADE DISTRIBUTIONS
78.3%
65.2%
52.2%
39.1%
26.1%
13.0%
0.0%
A+
A
A-
B+
B
B-
C+
C
C-
D+
D
D-
F

Grade distributions are collected using data from the UCLA Registrar’s Office.

ENROLLMENT DISTRIBUTIONS
Clear marks

Sorry, no enrollment data is available.

AD

Reviews (1)

1 of 1
1 of 1
Add your review...
Quarter: Winter 2024
Grade: A
Verified Reviewer This user is a verified UCLA student/alum.
June 22, 2024

Loved this class. The professor is super clear and concise in explaining difficult concepts, and it was the first presentation of machine learning concepts where I felt like I truly understood. We went over the basics including linear regression and perceptrons, but we also talked about more recent models including the architecture of diffusion models, transformer models, and even SORA.

Homeworks include theory problems, which are fine if you pay attention to the lectures, and some coding problems to get some practice with the theory. The final exam was essentially like the last homework.

Definitely recommend this class to anyone who's interested in learning more deeply about machine learning models.

Helpful?

0 0 Please log in to provide feedback.
Verified Reviewer This user is a verified UCLA student/alum.
Quarter: Winter 2024
Grade: A
June 22, 2024

Loved this class. The professor is super clear and concise in explaining difficult concepts, and it was the first presentation of machine learning concepts where I felt like I truly understood. We went over the basics including linear regression and perceptrons, but we also talked about more recent models including the architecture of diffusion models, transformer models, and even SORA.

Homeworks include theory problems, which are fine if you pay attention to the lectures, and some coding problems to get some practice with the theory. The final exam was essentially like the last homework.

Definitely recommend this class to anyone who's interested in learning more deeply about machine learning models.

Helpful?

0 0 Please log in to provide feedback.
1 of 1
5.0
Overall Rating
Based on 1 User
Easiness 3.0 / 5 How easy the class is, 1 being extremely difficult and 5 being easy peasy.
Clarity 5.0 / 5 How clear the class is, 1 being extremely unclear and 5 being very clear.
Workload 5.0 / 5 How much workload the class is, 1 being extremely heavy and 5 being extremely light.
Helpfulness 5.0 / 5 How helpful the class is, 1 being not helpful at all and 5 being extremely helpful.

TOP TAGS

There are no relevant tags for this professor yet.

ADS

Adblock Detected

Bruinwalk is an entirely Daily Bruin-run service brought to you for free. We hate annoying ads just as much as you do, but they help keep our lights on. We promise to keep our ads as relevant for you as possible, so please consider disabling your ad-blocking software while using this site.

Thank you for supporting us!