Eleazar Eskin
Department of Computer Science
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4.0
Overall Rating
Based on 1 User
Easiness 5.0 / 5 How easy the class is, 1 being extremely difficult and 5 being easy peasy.
Clarity 2.0 / 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 3.0 / 5 How helpful the class is, 1 being not helpful at all and 5 being extremely helpful.

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Quarter: Fall 2023
Grade: P
Verified Reviewer This user is a verified UCLA student/alum.
Dec. 21, 2023

Standard seminar class that's required for any CASB major/minor. Show up, take some brief notes, and write a review for each talk. The review is just a summary of the talk, the most recent developments, what you didn't understand, and feedback. Some of the talks were actually really cool and prompted several students to join the researchers' labs (my favorites were Neuroimaging Informatics, AI in Medicine, and Genetic & Phenotypic Psychiatry), but expect them to change year by year. If you're already in a lab and like the research you're doing, it's pretty boring, but there's practically no workload - if you sit at the back of the lecture hall, you will see a bunch of people doing homework, solving the NYT crossword, playing snake, or chatting while taking notes lol

The slightly annoying part was trying to summarize a boring talk that didn't make any sense, since some researchers assume that undergrads have a working knowledge of a bunch of statistics and ML stuff from the stats 100 or 101 series. But, even if you have a big-picture understanding and can at least name the methods they used without explaining then you're fine. Also I kinda hate genetics and like 70% of them were about it so that was also pretty annoying.

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Verified Reviewer This user is a verified UCLA student/alum.
Quarter: Fall 2023
Grade: P
Dec. 21, 2023

Standard seminar class that's required for any CASB major/minor. Show up, take some brief notes, and write a review for each talk. The review is just a summary of the talk, the most recent developments, what you didn't understand, and feedback. Some of the talks were actually really cool and prompted several students to join the researchers' labs (my favorites were Neuroimaging Informatics, AI in Medicine, and Genetic & Phenotypic Psychiatry), but expect them to change year by year. If you're already in a lab and like the research you're doing, it's pretty boring, but there's practically no workload - if you sit at the back of the lecture hall, you will see a bunch of people doing homework, solving the NYT crossword, playing snake, or chatting while taking notes lol

The slightly annoying part was trying to summarize a boring talk that didn't make any sense, since some researchers assume that undergrads have a working knowledge of a bunch of statistics and ML stuff from the stats 100 or 101 series. But, even if you have a big-picture understanding and can at least name the methods they used without explaining then you're fine. Also I kinda hate genetics and like 70% of them were about it so that was also pretty annoying.

Helpful?

0 0 Please log in to provide feedback.
1 of 1
4.0
Overall Rating
Based on 1 User
Easiness 5.0 / 5 How easy the class is, 1 being extremely difficult and 5 being easy peasy.
Clarity 2.0 / 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 3.0 / 5 How helpful the class is, 1 being not helpful at all and 5 being extremely helpful.

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There are no relevant tags for this professor yet.

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