Question about parameter posterior in bayesian linear regression

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At page 4 of cs229 section note (http://cs229.stanford.edu/section/cs229-gaussian_processes.pdf), I don't understand eq(2)

question

In this equation,

  • how do the $\theta$ is differentiated ? I think $\theta$ is just like that $ \theta = \begin{bmatrix} \theta^{(1)} \\ \vdots \\ \theta^{(n)} \\ \end{bmatrix} $ . $\theta^{'} = ? $

  • How does the equation is equal ? $ p(S|\theta) = \prod^m_{i=1} p(y^{(i)} | x^{(i)}, \theta) $

    • $p(S) = \prod^m_{i=1} p(x^{(i)}, y^{(i)})$ right?

The more condition is like that. condition