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UID:onejoy-event-49263@onejoy
DTSTAMP:20261009T153822Z
DTSTART:20260926T180000Z
DTEND:20260926T200000Z
SUMMARY:Probability and Machine Learning
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DESCRIPTION:Ever wonder why we train regression models on mean squared error?\nMost of us learned MSE as a rule to memorize: regression task → square the errors → minimize. But it's not an arbitrary choice. MSE falls straight out of probability theory — once you ask "what distribution generated this data?"\, the loss basically derives itself.\nIn this focused hour\, we'll trace exactly where MSE comes from:\n• How MSE emerges from a Gaussian likelihood\n• What "minimizing squared error" is really doing under the \nhttps://onejoy.io/event/49263
URL:https://onejoy.io/event/49263
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