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  1. Jan 2022
    1. 1.1 Bernoulli distribution

      $$ Y \sim f_{B}(y ; \theta)= \begin{cases}\theta^{y}(1-\theta)^{1-y} & \forall y \in\{0,1\} \\ 0 & \text { otherwise }\end{cases} $$

      $$E[Y]=\theta$$

      $$var(Y)=\theta(1-\theta)$$

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  2. Oct 2015
    1. full loss function as coming from a Gaussian prior over the weight matrix WW, where instead of MLE we are performing the Maximum a posteriori (MAP) estimation. We mention these interpretations to help your intuitions, but the full details of this derivation are beyond the scope of this class.

      Can anyone provide resources where I can find this derivation? In particular, the derivation for the regularization term \(R(W)\) coming from a Gaussian prior on \(W\).