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This lecture introduces the concept of supervised learning and discusses how to minimize population risk in a supervised learning setting. The lecture also covers the empirical risk minimization algorithm and how it can be used to find the best model for a given family of loss functions.
The video provides an overview of supervised learning and how to empirically minimize risk. It discusses the conditions that must be met in order for the Central Limit Theorem to apply.
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