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## Sriram Sankararaman

###### AD

**Overall Ratings**

Based on 28 Users

*/ 5*How easy the class is,

**1**being extremely difficult and

**5**being easy peasy.

*/ 5*How light the workload is,

**1**being extremely heavy and

**5**being extremely light.

*/ 5*How clear the professor is,

**1**being extremely unclear and

**5**being very clear.

*/ 5*How helpful the professor is,

**1**being not helpful at all and

**5**being extremely helpful.

This is an amazing class. I can't recommend Professor Sankararaman enough. He is able to distill complex ideas into easy-to-understand and interesting lectures. His slides are slick, clear, and thorough. He also posted lecture videos online our year. However, I highly recommend going to class because it is really easy to fall behind if you rely on just the videos. This class is quite difficult. If this is your first machine learning class, you will have to put in a significant amount of effort to truly understand the material and get an A. Before the class starts, I recommend going over your Math 32A and 33A notes. You should be comfortable with multivariable calculus and linear algebra. You should also have taken a proper probability and statistics course beforehand. The projects and homeworks are pretty interesting. You'll be exposed to many different ML models and techniques such as decision trees, linear/polynomial regression, SVMs, PCA, boosting, HMMs, and clustering.

Highly recommend this class for those wanting a better mathematical foundation in machine learning and knowledge of the basic algorithms. The homeworks are mostly math problems and proof related to machine learning concepts, and usually the last problem involves programming one of the algorithms you learned in class. The tests are pretty much the same, minus the programming parts.

Sriram was a fine professor. He could be a little eccentric and sometimes go too quickly with the concepts + math proofs, but for the most part he and his TAs did a good job of making sure you could understand the concepts and the homeworks. Overall I recommend him as a teacher.

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.

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

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.

This class is very theory and math/proof heavy, which I struggled with as a Data Engineering Minor. Long, long proofs in class for machine learning models, difficult to focus on. The homework assignments were some light math assignments, then Python coding. The midterm exam was easy, the final exam was the hardest exam of my entire life. If you need to take this class, this professor is a solid choice.

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

Go to lectures, they're good. Go to OH, Prof. Sriram is a nice guy. Take the homeworks seriously and start early, they take less than 5 hours each on average and there are only 4. Pretty frustrated to see people ask for extensions when they aren't really needed. (We have 2 ! weeks per pset). Midterm median was pretty high (it was also way too easy) at 93. Final exam was harder (relatively speaking, but not hard in general. Pset level of difficulty for the questions) with median of 74.5. We could have been downcurved this quarter if the average was higher. For that reason, I thank the DSE minors who took this class.

This is an amazing class. I can't recommend Professor Sankararaman enough. He is able to distill complex ideas into easy-to-understand and interesting lectures. His slides are slick, clear, and thorough. He also posted lecture videos online our year. However, I highly recommend going to class because it is really easy to fall behind if you rely on just the videos. This class is quite difficult. If this is your first machine learning class, you will have to put in a significant amount of effort to truly understand the material and get an A. Before the class starts, I recommend going over your Math 32A and 33A notes. You should be comfortable with multivariable calculus and linear algebra. You should also have taken a proper probability and statistics course beforehand. The projects and homeworks are pretty interesting. You'll be exposed to many different ML models and techniques such as decision trees, linear/polynomial regression, SVMs, PCA, boosting, HMMs, and clustering.

Highly recommend this class for those wanting a better mathematical foundation in machine learning and knowledge of the basic algorithms. The homeworks are mostly math problems and proof related to machine learning concepts, and usually the last problem involves programming one of the algorithms you learned in class. The tests are pretty much the same, minus the programming parts.

Sriram was a fine professor. He could be a little eccentric and sometimes go too quickly with the concepts + math proofs, but for the most part he and his TAs did a good job of making sure you could understand the concepts and the homeworks. Overall I recommend him as a teacher.

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.

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

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.

This class is very theory and math/proof heavy, which I struggled with as a Data Engineering Minor. Long, long proofs in class for machine learning models, difficult to focus on. The homework assignments were some light math assignments, then Python coding. The midterm exam was easy, the final exam was the hardest exam of my entire life. If you need to take this class, this professor is a solid choice.

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

Go to lectures, they're good. Go to OH, Prof. Sriram is a nice guy. Take the homeworks seriously and start early, they take less than 5 hours each on average and there are only 4. Pretty frustrated to see people ask for extensions when they aren't really needed. (We have 2 ! weeks per pset). Midterm median was pretty high (it was also way too easy) at 93. Final exam was harder (relatively speaking, but not hard in general. Pset level of difficulty for the questions) with median of 74.5. We could have been downcurved this quarter if the average was higher. For that reason, I thank the DSE minors who took this class.