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- Sriram Sankararaman
- COM SCI M146

###### AD

**Overall Rating**

Based on 20 Users

*/ 5*How easy the class is,

**1**being extremely difficult and

**5**being easy peasy.

*/ 5*How clear the class is,

**1**being extremely unclear and

**5**being very clear.

*/ 5*How much workload the class is,

**1**being extremely heavy and

**5**being extremely light.

*/ 5*How helpful the class is,

**1**being not helpful at all and

**5**being extremely helpful.

#### TOP TAGS

- Uses Slides

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.

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

Sorry, no enrollment data is available.

###### AD

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.

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.

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.

**Overall Rating**

Based on 20 Users

*/ 5*How easy the class is,

**1**being extremely difficult and

**5**being easy peasy.

*/ 5*How clear the class is,

**1**being extremely unclear and

**5**being very clear.

*/ 5*How much workload the class is,

**1**being extremely heavy and

**5**being extremely light.

*/ 5*How helpful the class is,

**1**being not helpful at all and

**5**being extremely helpful.

#### TOP TAGS

- Uses Slides (10)