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I. Foundation
II. Linear Models
III. Deep Neural Networks
IV. Non Parametric Models
V. Beyond Supervised Learning

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4.1 Intro

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4. Statistics

4.1 Intro

4. Statistics

4.1 Intro

We estimate probability models parameters $\theta$ from data $\mathcal{D}$ . Most methods are optimizations of the form:

\begin{equation} \hat{\theta}=\argmin_{\theta} \mathcal{L}(\theta) \end{equation}

Home 4.2 Maximum Likelihood Estimation