Unit 6 8 Supervised vs Unsupervised LearningYouTube↗  IA↗(long shot)
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So, congratulations.
You just made it through the unsupervised learning section of this class.
I think you've learned a lot.
You learned about K-means, you learned about expectation maximization,
about dimensionality reduction and even spectral clustering.
The first 3 items--K-means, EM, and dimensionality reduction--
are used very frequently, and spectral clustering is a rarer used method
that shows some of the most recent research going on in the field.
I hope you have fun applying these methods in practice.
I'd like to say a few final words about supervised versus unsupervised learning.
In both cases you're given data, but in 1 case you have labeled data,
in another you have unlabeled data.
The supervised learning paradigm is the dominant paradigm in machine learning,
and there are a vast amount of papers being written about it.
We talked about classification and regression
and different methods to do supervised learning.
The unsupervised paradigm is much less explored,
even though I think it's at least equally important--possibly even more important.
Many systems can collect vast amounts of data such as web crawlers,
robots, I told you about street view,
and getting the data is cheap, but getting labels is hard.
So to me, unsupervised is the method of the future.
It's one of the most interesting open research topics
to see whether we can make sense out of large amounts of unlabeled or poorly labeled data.
In between, there are techniques that do both: supervised and unsupervised.
They are called semi-supervised or self-supervised,
and they use elements of unsupervised learning and pair them with supervised learning.
Those are fascinating by their own rights.
Our robot Stanley, for example, that won the DARPA Grand Challenge
used its own sensors to produce labels on the fly to other data.
And I'll talk about this when I talk about robotics in more detail.
But for the time being, understand that the paradigms supervised and unsupervised
span 2 very large areas of machine learning, and you learn quite a bit about it.
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