Vwani P Roychowdhury
Department of Electrical Engineering
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3.0
Overall Rating
Based on 2 Users
Easiness 4.0 / 5 How easy the class is, 1 being extremely difficult and 5 being easy peasy.
Clarity 1.5 / 5 How clear the class is, 1 being extremely unclear and 5 being very clear.
Workload 3.5 / 5 How much workload the class is, 1 being extremely heavy and 5 being extremely light.
Helpfulness 3.0 / 5 How helpful the class is, 1 being not helpful at all and 5 being extremely helpful.

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GRADE DISTRIBUTIONS
71.2%
59.4%
47.5%
35.6%
23.7%
11.9%
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.

76.2%
63.5%
50.8%
38.1%
25.4%
12.7%
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.

77.7%
64.7%
51.8%
38.8%
25.9%
12.9%
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.

69.2%
57.7%
46.2%
34.6%
23.1%
11.5%
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.

61.3%
51.1%
40.8%
30.6%
20.4%
10.2%
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.

77.1%
64.2%
51.4%
38.5%
25.7%
12.8%
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.

70.8%
59.0%
47.2%
35.4%
23.6%
11.8%
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.

60.7%
50.6%
40.5%
30.4%
20.2%
10.1%
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.

100.0%
83.3%
66.7%
50.0%
33.3%
16.7%
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.

77.5%
64.6%
51.7%
38.8%
25.8%
12.9%
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.

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Reviews (2)

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

Class is graded on just four group projects which is nice, but they are a decent amount of work. Lectures felt really disorganized, so I ended up not going after a few but was fine in doing the projects. It was nice that I got some hands-on work with the projects, but I didn't learn much theory from the lectures.

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Quarter: Winter 2023
Grade: A
Verified Reviewer This user is a verified UCLA student/alum.
April 5, 2023

This class is more or less a high level overview of machine learning. It covers common tools for analysis and feature extraction like dimensionality reduction and goes over common ML models. Some examples here include Naive Bayes, SVMs, decision trees, neural networks, etc. The coursework load is fairly light for an engineering class with 4 projects, which are long and reasonably well-guided assignments. These can be done in a group or alone, where I opted for the latter and would generally recommend that as it is quite doable and you learn more this way than by carving them up.

Regarding the lectures and Prof. Roychowdhury, I generally found them a bit disorganised and did not engage with them much. Roughly 3 lectures in I pretty much focused only on the assignments and was fine. Said assignments I very much enjoyed though, as they were heavy on programming and analysis, which I wanted to practice. They were not particularly mathematically rigorous though, so I would recommend ECE 246 "Foundations of Statistical Machine Learning", by Prof. Diggavi for that.

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

Class is graded on just four group projects which is nice, but they are a decent amount of work. Lectures felt really disorganized, so I ended up not going after a few but was fine in doing the projects. It was nice that I got some hands-on work with the projects, but I didn't learn much theory from the lectures.

Helpful?

0 0 Please log in to provide feedback.
Verified Reviewer This user is a verified UCLA student/alum.
Quarter: Winter 2023
Grade: A
April 5, 2023

This class is more or less a high level overview of machine learning. It covers common tools for analysis and feature extraction like dimensionality reduction and goes over common ML models. Some examples here include Naive Bayes, SVMs, decision trees, neural networks, etc. The coursework load is fairly light for an engineering class with 4 projects, which are long and reasonably well-guided assignments. These can be done in a group or alone, where I opted for the latter and would generally recommend that as it is quite doable and you learn more this way than by carving them up.

Regarding the lectures and Prof. Roychowdhury, I generally found them a bit disorganised and did not engage with them much. Roughly 3 lectures in I pretty much focused only on the assignments and was fine. Said assignments I very much enjoyed though, as they were heavy on programming and analysis, which I wanted to practice. They were not particularly mathematically rigorous though, so I would recommend ECE 246 "Foundations of Statistical Machine Learning", by Prof. Diggavi for that.

Helpful?

0 0 Please log in to provide feedback.
1 of 1
3.0
Overall Rating
Based on 2 Users
Easiness 4.0 / 5 How easy the class is, 1 being extremely difficult and 5 being easy peasy.
Clarity 1.5 / 5 How clear the class is, 1 being extremely unclear and 5 being very clear.
Workload 3.5 / 5 How much workload the class is, 1 being extremely heavy and 5 being extremely light.
Helpfulness 3.0 / 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.

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