ORIE4741 2020 Sign Up Sheet
Welcome to ORIE 4741! Please fill out this form so we get a sense of who's taking the class, regardless of whether you've registered or not. We will use the emails of those who register via this form to populate our campuswire instance, so you can ask questions on the class Q&A there.
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Email *
What is your name? *
What is your major or field of study? *
What is your class year? *
What time zone will you reside in as of the first day of the semester? *
Will you be able to attend the class synchronously (ie, log on to reliable internet at 9:55am ET)? Recall that the participation requirement (15% of course grade) is slightly harder for async participants: students taking the class synchronously will respond to in-class polls to receive participation credit; students watching the class asynchronously will complete a short writing assignment in addition to answering the poll questions. *
The course will have weekly quizzes on the lectures, which you'll be able to take any time in a 24 hour window. Lectures are TTh. What day should the weekly quiz start? *
The course will have weekly quizzes, which you'll be able to take any time in a 24 hour window. What time should the weekly quiz start? *
Time
:
Have you taken a Machine Learning, Data Science, or Statistical Learning course? *
How familiar are you with the prerequisites of 1) linear algebra 2) a modern scripting language, and 3) probability? See the course webpage for more details. *
List the most closely related related courses you've taken. (No more than 3, please.) *
Are you currently enrolled in the class? *
How certain are you that you'll take this class if you can enroll? *
Will definitely drop
Will definitely take
Will you be taking this class for credit? *
Would you be willing to be a (paid) grader for the course? Graders can choose to work between 2 and 7.5 hours each week, and enable us to offer the course to more students. (If you might be willing, but have a question, write it in "Other".) *
What's the big messy data set you'd most like to understand?
Any topics in particular you're excited to learn about?
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