Old Potions Master's habit, you know

Let's show some examples of machine learning problems and I want you to tell me, for each one, whether it's best addressed with supervised learning, unsupervised learning, or reinforcement learning.

In this video I am going to define what is probably the most common type of machine

Given this data set, an

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

learning. I'll define what these terms mean more in the next couple videos.

By now, you see the range of different learning algorithms.

Peter will tell you some more about machine learning.

learning algorithms. There are several different types of learning algorithms.

For each of the four check boxes on the left, check the ones for which you think Unsupervised Learning algorithm would be appropriate and then click the button on the lower right to check your answer.

And these machine learning techniques are the enabling techniques that make that possible.

We often just observe.

Every machine learning method is driven by some sort of target information that you care about.

In this video, we'll talk about the second major type of machine

So, that's it for supervised learning and in the next video I'll talk about unsupervised learning, which is the other major category of learning algorithms.

So I have a rat who is trained to press a lever to get a release of food when certain conditions are met.

And one of the interesting aspects is that it's mainly for unsupervised problem, that there's some aspects of it that are also very similar to sort of the supervised learning problem.

So, congratulations.

And I'll talk about this when I talk about robotics in more detail.

I hope you have fun applying these methods in practice.

You ever fool around with one of your teachers?

We talked a lot about supervised learning in which we are given data and target labels.

In this class we'll talk about supervised

How you were his high school teacher.

In both cases you're given data, but in 1 case you have labeled data, in another you have unlabeled data.

If you want to teach someone a new language you might start by teaching them vocabulary words and grammatical rules that explain how to construct sentences.

And that's why the points plotted up on the figure don't have any labels on them.

learning problem, called Unsupervised Learning.

And just to remind you the other most common type of supervised

I will show you a new approach to foreign language learning.

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

One over here, and one over here, and our data looks as follows.

Next example is analyzing the spectral emissions of stars and trying to find clusters of stars in dissimilar types that may be of interest to astronomers.

But this was tedious work for the experts, so only a small number of states were labelled.

I'll give the materials, I'll come back and test them they get another zero,

Battered, bruised, you start again.

Use the Leitner learning method

We've just rolled this out in the last couple months

I'm a school teacher.

Ask Jeeves, etc.

And so I'd like to take the next videos to talk about that.

So we can apply that same process, even though it's not supervised.

Let's stop and review the 3 main forms of learning.

But each of these would be a fine example of a

And because there's a small number of discrete values, I would therefore treat it as a classification problem.