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Maximum A Posteriori (MAP) - Why L2 Regularization is Bayesian in Disguise
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Last Updated: September 28, 2026
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Summary
Explains Maximum Likelihood (ML) and Recall that learning from data given a model class f involves finding a good set of parameters. How should we do this? Intro to ... If you flip a coin three times and get heads every time, does that really mean the coin always lands heads? In this video we show how to incorporate prior information into the least squares regression, consistent with the framework of ... Notes: robosathi.com/docs/maths/probability/parametric-model- To along with the course, visit the course website: web.stanford.edu/class/archive/cs/cs109/cs109.1232/ Chris Piech ... If you hang out around statisticians long enough, sooner or later someone is going to mumble "