We talked about classification and regression and different methods to do supervised learning.

learning problem, which is supervised learning.

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

Is that supervised, unsupervised or reinforcement learning?

So here's how this supervised learning algorithm works.

learning algorithm. So this is an example of a supervised learning algorithm.

In the last video, we talked about Supervised Learning.

So this class which is about supervised learning will go through some very basic methods for learning models from data in particular, specific types of Bayes Networks.

Within supervised learning, the performance of many supervised learning algorithms will be pretty similar and when that is less more often be whether you use

learning at the beginning of the class but it's useful to contrast it with supervised learning.

So this is an example of a supervised learning algorithm. And it's supervised learning because we're given the, quotes, right answer for each of our examples.

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

Support vector machines are supervised learning models used for classification and regression analysis.

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

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

And yes, Supervised Learning algorithm may break these data into these two separate clusters.

Consider a supervised learning classification problem where you have a training set like this.

We'll talk about supervised learning, which is one side of machine learning, and Peter will tell you about unsupervised learning, which is a different style.

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.

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.

Let me back up a step further, and let's look at supervised learning more generally.

Here's another supervised learning example, some friends and I were actually working on this earlier.

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

We have supervised learning, in which the training set is a bunch of input output pairs

The supervised learning paradigm is the dominant paradigm in machine learning, and there are a vast amount of papers being written about it.

The program tried to generalize from that, using supervised learning, and was not able to perform very well.

And that looks just like what we did when we used supervised machine learning to update our waits.

Machine learning is a very large field with many different methods and many different applications.

We're now using multiple methods. We use histogram methods and particle methods.

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The supervised learning problem in this case is given a set of labels to fit a hypothesis to it.

More formally, in supervised learning, we have a data set and this data set is called a training set.

Of the following four examples, which ones, which of these four do you think would will be an Unsupervised Learning algorithm as opposed to Supervised Learning problem.

So for each example in Supervised

Is that supervised, unsupervised or reinforcement?

Any methods that you could recommend to us for dealing with learning a lot of complex material?

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