So, what is unsupervised learning?

I briefly talked about unsupervised

In contrast, in the unsupervised

learning problem, called Unsupervised Learning.

So, that's it for Unsupervised

Is that supervised, unsupervised or reinforcement?

In observing, we apply unsupervised learning techniques.

In unsupervised learning we're just given data.

Now this is a really complicated unsupervised

Would that be supervised, unsupervised or reinforcement?

Is that supervised, unsupervised or reinforcement learning?

And this will be our first type of unsupervised learning, although there will be other types of unsupervised

There are many other examples of unsupervised learning.

Narrator So welcome to the class on unsupervised learning.

Recently, its teachers let it surf the Internet unsupervised.

So one of the fascinating, unsolved, unsupervised learning tasks is

Clustering. Clustering is the most basic form of unsupervised learning.

Narrator So to start with some lingo about unsupervised learning.

Unsupervised learning can be applied to find structure and data.

So to me, unsupervised is the method of the future.

In between, there are techniques that do both supervised and unsupervised.

Unsupervised Learning or clustering is used for a bunch of other applications.

The main two types are what we call supervised learning and unsupervised

You just made it through the unsupervised learning section of this class.

So, that's clustering which is our first example of an unsupervised learning algorithm.

Unsupervised Learning algorithm might decide that the data lives in two different clusters.

I'd like to say a few final words about supervised versus unsupervised learning.

Here is one of my favorite examples of unsupervised learning one that is yet unsolved.

All of these are examples of clustering, which is just one type of Unsupervised Learning.

So the task of unsupervised learning is to find structure in data of this type.

Then we have unsupervised learning, in which we're given just a set of data points

Let's now talk about a 2nd class of unsupervised learning avenues that are called dimensionality reduction.

It turns out that clustering algorithms and Unsupervised Learning algorithms are used in many other problems as well.

We will complement this with a class on unsupervised learning that will be taught next after this class.

In the next video, I'm going to define what is supervised learning and after that, what is unsupervised learning.

Because we're not giving the algorithm the right answer for the examples in my data set, this is Unsupervised Learning.

But we have to let the algorithm discover all this just from the data. Finally, it turns out that Unsupervised

The unsupervised paradigm is much less explored, even though I think it's at least equally important possibly even more important.

So you might look at an Unsupervised Learning algorithm like this and ask how complicated this is to implement this, right?

So a good chunk of unsupervised learning seeks to recover the underlying the density of probability distribution that generated the data.

And for unsupervised learning of the K means I'm going to use the convention that XI is an RN dimensional vector.

They are called semi supervised or self supervised, and they use elements of unsupervised learning and pair them with supervised learning.

Even though we haven't been told about anything in unsupervised learning, I'd like to quiz your intuition on the following 2 questions

We also talk about unsupervised learning where target labels are missing and we use replacement principles to find, for example hidden concepts.

This will be exciting because this is our first unsupervised learning algorithm where we learn from unlabeled data instead of the label data.