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In this module we've covered a lot of ground.
To start with we motivated taking a probabilistic model-based
approach to clustering, and
showed mixtures of Gaussians as a special example of such an approach.
Then we presented the Algorithm, which is a very generally useful algorithm, and
we specified it specifically for mixtures of Gaussians.
And throughout the module we've compared and contrasted these model based
approaches with the K-means algorithm that we described in the last module.
And what we saw in this module is that you can actually view K-means
as a special case of For mixtures of Gaussians.
So, thinking about mixtures of Gaussians and
In order to infer our cluster parameters and our soft assignments,
really is a generalization of the K-means algorithm that we showed before.
But there is a cost to it, because there are a lot more parameters that we have
to think about learning from data.
And in addition, there's a computational cost to doing Instead of K-means,
both when we're computing our responsibilities and
when we're estimating our model parameters.
Each of these steps is more intensive than the steps that you
have to perform in K-means.
So it is a trade-off in terms of flexibility and
the descriptive output you get.
You get the soft assignments capturing uncertainty and
you can do different things with that, but there is a cost to it.
And in summary, I'll just leave you with a list of things that you should be able to
do having watched this module.
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