- Gaussian/Normal Distribution

- sigma = one standard deviation
- Gaussian = Range of Doubtness
- probability x is depend on the mew and sigma parameters
- Fixed formula and no need to be remembered

- Less the sigma, higher the probability, make a narrower and higher graph
- more sigma, more doubtness, fatter gaussian distribution

- we want to plot the Gaussian distribution of every examples
- The problem is we don't know what to set mew and sigma squared parameter
- Or maybe we can set the parameters?
- We can set the parameters based on the distance of the parameters gathered
- mew = average of the example
- sigma squared = is the range of error. how much the variance of x from the average(mew) value. Summation of range error between x and amew
- Maximum Likelihood Estimate is the name of formula above
- ML tend to use m training set rather than m-1

SUMMARY

- Given a training set, now we can set mew and sigma for Gaussian Distribution
- Next how to implement GD in Anomaly Detection