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I think it's more that machine learning builds off of earlier work in statistics. If I remember correctly, that class discusses lots of other things that are used in ML but were invented elsewhere (gradient descent, maximum likelihood, matrix methods, etc.)

Arguably this is all just semantics (nature knows no stats/ML divide), but as a ML person I know this drives statistics folks crazy.




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