likes action prefers blockbus ters Viewer taste movie content imply viewer

Likes action prefers blockbus ters viewer taste movie

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likes action? prefers blockbus ters? Viewer taste & movie content imply viewer rating. No magical formula to predict viewer rating. Netflix has data. We can learn to identify movie “categories” as well as viewer “preferences” Class Motto: A p atte rn exists. We don’t know it. We have d ata to learn it.
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Credit Approval Let’s use a conceptual example to crystallize the issues. age 32 years gender male salary 40,000 debt 26,000 years in job 1 year years at home 3 years . . . . . . Approve for credit?
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Credit Approval Let’s use a conceptual example to crystallize the issues. Using salary, debt, years in residence, etc., approve for credit or not. No magic credit approval formula. Banks have lots of data. customer information: salary, debt, etc. whether or not they defaulted on their credit. age 32 years gender male salary 40,000 debt 26,000 years in job 1 year years at home 3 years . . . . . . Approve for credit? A p att ern exists. We don’t know it. We have dat a to learn it.
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What is Machine Learning? The term Machine Learning is used to characterize a number of different approaches for generalizing from observed data: Supervised learning - Given a set of features and labels learn a model that will predict a label to a new feature set
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  • Fall '17
  • Mark Isbel

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