There is no data-generating distribution
Read OriginalThe article is a reflection from a professor's final lecture on machine learning, arguing against the standard assumption of a 'data-generating distribution.' It discusses how removing this and other field-making myths from the curriculum leads to a more honest, distribution-free understanding of ML, focusing on populations, samples, and the engineer's role in creating or imagining randomness.
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