Sriram Sankararaman
Department of Computer Science
AD
4.4
Overall Rating
Based on 20 Users
Easiness 2.8 / 5 How easy the class is, 1 being extremely difficult and 5 being easy peasy.
Clarity 4.2 / 5 How clear the class is, 1 being extremely unclear and 5 being very clear.
Workload 3.1 / 5 How much workload the class is, 1 being extremely heavy and 5 being extremely light.
Helpfulness 4.4 / 5 How helpful the class is, 1 being not helpful at all and 5 being extremely helpful.

TOP TAGS

  • Uses Slides
GRADE DISTRIBUTIONS
26.1%
21.7%
17.4%
13.0%
8.7%
4.3%
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.

21.9%
18.2%
14.6%
10.9%
7.3%
3.6%
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.

26.7%
22.3%
17.8%
13.4%
8.9%
4.5%
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.

55.1%
45.9%
36.7%
27.5%
18.4%
9.2%
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.

25.6%
21.3%
17.1%
12.8%
8.5%
4.3%
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.

24.8%
20.7%
16.5%
12.4%
8.3%
4.1%
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.

ENROLLMENT DISTRIBUTIONS
Clear marks

Sorry, no enrollment data is available.

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Reviews (12)

1 of 2
1 of 2
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Quarter: Winter 2021
Grade: A
COVID-19 This review was submitted during the COVID-19 pandemic. Your experience may vary.
March 29, 2021

Professor Sankararaman is an amazing lecturer, and he explains concepts in a clear and concise manner. For my quarter, there were eight quizzes in total (pretty much one per week), which was somewhat inconvenient. However, I learned a lot from this class, and it was overall a highly enjoyable experience. I would definitely recommend taking this class with this professor.

Helpful?

2 0 Please log in to provide feedback.
Quarter: Winter 2019
Grade: N/A
April 1, 2019

grades were curved down. homework 50%, midterm 20%, and final 30%. if ur exam scores on midterm and final are higher than the median, and did all the homework, u should secure a B+ at least. not a easy class if u have no prior knowledge in ML.

Helpful?

1 0 Please log in to provide feedback.
Quarter: Winter 2023
Grade: A+
Verified Reviewer This user is a verified UCLA student/alum.
April 5, 2023

Lectures are clear and slides are provided.

There are easy quizzes most weeks.
There are easy problem sets with coding sections.
The final was easy compared to the EC ENGR C147 midterm (took in same quarter).
C147 helps with M146 more than M146 helps with C147.
Machine learning topics aren't viewed from a big-picture perspective and aren't really related to each other from week to week.
Machine learning topics are pretty boring.

Helpful?

0 0 Please log in to provide feedback.
Quarter: Winter 2023
Grade: A
April 2, 2023

Overall a great intro class to ML, a good prep course for C147

Helpful?

0 0 Please log in to provide feedback.
Quarter: Winter 2023
Grade: N/A
March 28, 2023

Didn't attend a single lecture but the class is popular and well-structured. The TAs are very knowledgeable and very quick in grading. ML concepts are boring though.

Helpful?

0 1 Please log in to provide feedback.
Quarter: Winter 2022
Grade: A
COVID-19 This review was submitted during the COVID-19 pandemic. Your experience may vary.
June 23, 2022

echo the rest of the reviews this man is the goat

Helpful?

0 0 Please log in to provide feedback.
Quarter: Winter 2021
Grade: A
COVID-19 This review was submitted during the COVID-19 pandemic. Your experience may vary.
April 6, 2021

Class structure: Problem sets every two weeks, quizzes every week, final at the end
Lectures: engaging prof and great slides mean lectures were (mostly) easy to listen to. I watched them a day after on 1.25 or 1.5 speed, and that was great for me. I downloaded all the slides to reference later on.
PSets: if you watched lectures, know how to do some advanced calculus, and have a copy of the slides handy, you should be able to do all the conceptual problems in these PSets (roughly 50% of the points on each PSet). Additionally, they encourage you to discuss with other classmates to "check answers" and whatnot. The coding portions at the end of the PSets (50%) are super fun and straightforward. They give very detailed instructions and you get to see how machine learning works on real datasets.
Quizzes: also very straightforward. Plugging numbers into formulas (have to know which formula to use!) and having the sides handy to ctrl+F is imperative. I found that there was more than enough time to answer each question. They are all multiple choice.
Final: basically a long quiz.

Nice prof, cool material, good format

Helpful?

0 0 Please log in to provide feedback.
Quarter: Winter 2020
Grade: A
Sept. 13, 2020

Take it. The professor is very passionate about teaching and give clear instructions on what is going on every lecture. The slides are not very creative but they are clear enough even for self-study. The homework and problem sets are fairly assigned and graded. This class does not involve a lot of coding, so it is one of the easiest CS upper I have ever taken. He will give skeleton code, and all you have to do is to fill in the "to do" parts according to instructions, where everything is done in python. For lectures and exams, I feel it is more math and stats focused, but with the foundation is Stats 100A or other equivalents, one will be fine on those stuff. Honestly, easy A for a CS upper.

Helpful?

0 0 Please log in to provide feedback.
Quarter: Winter 2019
Grade: A
April 29, 2019

This is a great class to take. The concepts are covered very well and the tests and homework’s are very fair. The material gets harder after week 5 as you do kernels and SVMs, so make sure to keep attending lecture. The grading scheme is tough so make sure you don’t lose points on the homework

Helpful?

0 0 Please log in to provide feedback.
Quarter: Winter 2019
Grade: A-
April 1, 2019

Siriam is an awesome professor. The class is very well-organized. There are several TAs who each hold lots of office hours throughout the week. The only complaint I have is grades were curved down at the end of the quarter.

Helpful?

0 0 Please log in to provide feedback.
COVID-19 This review was submitted during the COVID-19 pandemic. Your experience may vary.
Quarter: Winter 2021
Grade: A
March 29, 2021

Professor Sankararaman is an amazing lecturer, and he explains concepts in a clear and concise manner. For my quarter, there were eight quizzes in total (pretty much one per week), which was somewhat inconvenient. However, I learned a lot from this class, and it was overall a highly enjoyable experience. I would definitely recommend taking this class with this professor.

Helpful?

2 0 Please log in to provide feedback.
Quarter: Winter 2019
Grade: N/A
April 1, 2019

grades were curved down. homework 50%, midterm 20%, and final 30%. if ur exam scores on midterm and final are higher than the median, and did all the homework, u should secure a B+ at least. not a easy class if u have no prior knowledge in ML.

Helpful?

1 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

Lectures are clear and slides are provided.

There are easy quizzes most weeks.
There are easy problem sets with coding sections.
The final was easy compared to the EC ENGR C147 midterm (took in same quarter).
C147 helps with M146 more than M146 helps with C147.
Machine learning topics aren't viewed from a big-picture perspective and aren't really related to each other from week to week.
Machine learning topics are pretty boring.

Helpful?

0 0 Please log in to provide feedback.
Quarter: Winter 2023
Grade: A
April 2, 2023

Overall a great intro class to ML, a good prep course for C147

Helpful?

0 0 Please log in to provide feedback.
Quarter: Winter 2023
Grade: N/A
March 28, 2023

Didn't attend a single lecture but the class is popular and well-structured. The TAs are very knowledgeable and very quick in grading. ML concepts are boring though.

Helpful?

0 1 Please log in to provide feedback.
COVID-19 This review was submitted during the COVID-19 pandemic. Your experience may vary.
Quarter: Winter 2022
Grade: A
June 23, 2022

echo the rest of the reviews this man is the goat

Helpful?

0 0 Please log in to provide feedback.
COVID-19 This review was submitted during the COVID-19 pandemic. Your experience may vary.
Quarter: Winter 2021
Grade: A
April 6, 2021

Class structure: Problem sets every two weeks, quizzes every week, final at the end
Lectures: engaging prof and great slides mean lectures were (mostly) easy to listen to. I watched them a day after on 1.25 or 1.5 speed, and that was great for me. I downloaded all the slides to reference later on.
PSets: if you watched lectures, know how to do some advanced calculus, and have a copy of the slides handy, you should be able to do all the conceptual problems in these PSets (roughly 50% of the points on each PSet). Additionally, they encourage you to discuss with other classmates to "check answers" and whatnot. The coding portions at the end of the PSets (50%) are super fun and straightforward. They give very detailed instructions and you get to see how machine learning works on real datasets.
Quizzes: also very straightforward. Plugging numbers into formulas (have to know which formula to use!) and having the sides handy to ctrl+F is imperative. I found that there was more than enough time to answer each question. They are all multiple choice.
Final: basically a long quiz.

Nice prof, cool material, good format

Helpful?

0 0 Please log in to provide feedback.
Quarter: Winter 2020
Grade: A
Sept. 13, 2020

Take it. The professor is very passionate about teaching and give clear instructions on what is going on every lecture. The slides are not very creative but they are clear enough even for self-study. The homework and problem sets are fairly assigned and graded. This class does not involve a lot of coding, so it is one of the easiest CS upper I have ever taken. He will give skeleton code, and all you have to do is to fill in the "to do" parts according to instructions, where everything is done in python. For lectures and exams, I feel it is more math and stats focused, but with the foundation is Stats 100A or other equivalents, one will be fine on those stuff. Honestly, easy A for a CS upper.

Helpful?

0 0 Please log in to provide feedback.
Quarter: Winter 2019
Grade: A
April 29, 2019

This is a great class to take. The concepts are covered very well and the tests and homework’s are very fair. The material gets harder after week 5 as you do kernels and SVMs, so make sure to keep attending lecture. The grading scheme is tough so make sure you don’t lose points on the homework

Helpful?

0 0 Please log in to provide feedback.
Quarter: Winter 2019
Grade: A-
April 1, 2019

Siriam is an awesome professor. The class is very well-organized. There are several TAs who each hold lots of office hours throughout the week. The only complaint I have is grades were curved down at the end of the quarter.

Helpful?

0 0 Please log in to provide feedback.
1 of 2
4.4
Overall Rating
Based on 20 Users
Easiness 2.8 / 5 How easy the class is, 1 being extremely difficult and 5 being easy peasy.
Clarity 4.2 / 5 How clear the class is, 1 being extremely unclear and 5 being very clear.
Workload 3.1 / 5 How much workload the class is, 1 being extremely heavy and 5 being extremely light.
Helpfulness 4.4 / 5 How helpful the class is, 1 being not helpful at all and 5 being extremely helpful.

TOP TAGS

  • Uses Slides
    (10)
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