mohlerm / eth-cil-exam-summary

Exam summary for Computational Intelligence Lab, ETH Zurich, SS16
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Final steps / thoughts #20

Closed mohlerm closed 8 years ago

mohlerm commented 8 years ago

So we're coming close to the exam :)

Is there anything you think that should be added or modified? As discussed with @passuf, I think it would make sense to try to finish the exam summary until 16:00 (UTC+2). @kilianrisse @Robin-des-Bois

Robin-des-Bois commented 8 years ago

Some possibilities:

What do you think?

mohlerm commented 8 years ago
Robin-des-Bois commented 8 years ago

For EM I would like to have something along the lines of

\subsection*{Latent variable}
We denote the latent variable indicating the component the point is sampled from by Z, which takes on values in $\{1,...,k\}$.

\subsection*{E-step: Posterior probabilities}
$\gamma_j^t(x_i) = P(Z=j|x_i, \theta_t) = \frac{P(x_i|Z=j, \theta_t) P(Z=j|\theta_t)}{Z}$

\subsection*{M-step: maximizing expected log likelihood}
$\mathbb{E}_{\gamma^t}[\log P(\mathcal{D;\theta})] = 
\mathbb{E}_{\gamma^t}[\log \Pi_{i=1}^nP(x_i,z_i;\theta)] = $ \\
$\sum_{i=1}^n \mathbb{E}_{\gamma^t}[\log P(x_i,z_i;\theta)] = $ \\
$\sum_{i=1}^n \sum_{j=1}^k \gamma_j^t(x_i) \log (P(x_i|z_i=j;\theta) P(z_i=j;\theta))$ \\
$\theta_{t+1} = \underset{\theta}{\operatorname{argmax}} \mathbb{E}_{\gamma^t}[\log P(\mathcal{D;\theta})]$

But it would need quite some space. :(

kilianrisse commented 8 years ago

I don't really think this is gonna fit on the summary and for me personally this is not of big use; I think the other stuff on the summary is more important.

mohlerm commented 8 years ago

I think most of these things are mentioned implicitely already and yeah, there isn't really space for it.