Question: For example, we always assumed that the data or signal error is a Gaussian distribution? why?

I have asked this question on stackoverflow, the link: <https://stackoverflow.com/questions/12616406/anyone-can-tell-me-why-we-always-use-the-gaussian-distribution-in-machine-learni>

Answer: 
Machine learning (and statistics as well) treats data as the mix of deterministic (causal) and random parts. The random part of data usually has normal distribution. (Really, the causal relation is reverse: the distribution of random part of variable is called normal). Central limit theorem says that the sum of large number of varibles each having a small influence on the result approximate normal distribution. 
1. Why data is treated as normally distributed? In machine learning we want to express dependent variable as some function of a number of independent variables. If this function is sum (or expressed as a sum of some other funstions) and we are suggesting that the number of independent variables is really high, then the dependent variable should have normal distribution (due to central limit theorem).
2. Why errors are looked to be normally distributed? The dependent variable ($Y$) consists of deterministic and random parts. In machine learning we are trying to express deterministic part as a sum of deterministic independent variables: $$deterministic + random = func(deterministic(1))+...+func(deterministic(n))+model\\_error$$ If the whole deterministic part of $Y$ is explained by $X$ then the $model\\_error$ depicts only $random$ part, and thus should have normal distribution. So if error distribution is normal, then we may suggest that the model is successful. Else there are some other features that are absent in model, but have large enough influence on $Y$ (the model is incomplete) or the model is incorrect.
i'm currently studying machine learning and the same question popped to my mind. What I think the reason should be is that in every machine learning problem we assume we have abundant observational data available and whenever data tends to infinity it gets normally distributed around its mean and thats what Normal distribution(Gaussian Distribution) says. Although its not necessary that Gaussian DIstribution will always be a perfect fit to any data that tends to infinity like take a case when your data is always positive then you can easily see if you try to fit gaussian dist to it it will give some weight to negative values of x also(although negligible but still some weight is given) so in such case distribution like Zeta is more suited.