Let's consider the problem of computer vision.

One final note

Machine learning algorithms for data mining tasks

Andrew teaches one of the bigger Stanford classes.

Here you can see some of the inner workings, where you can see a corridor that the vehicle attempts to go.

This fall quarter, Stanford University will be offering online for free, the Machine Learning class that I teach.

What is machine learning? In this video we will try to define what it is and also try to give you a sense of when you want to use machine learning.

Welcome to the machine learning unit.

To make sense out of the data, to extract information from the data, machine learning is the discipline to go.

Most of my spam I never see, but who knows what's going to happen with the future?

So that we can get a sense of what we can expect them to do.

So congratulations.

Peter will tell you some more about machine learning.

There's books written about it and courses taught about it.

In the next video, we'll start to give a more formal definition of what is machine learning.

The most common prototyping language I see people use for machine

They see about 25 meters out, so to drive really fast the robot has to see further.

It's really great about the real world as we gain more data like the world wide web or medical data sets or financial data sets.

Support vector machines, a technique from machine

But perhaps the most important invention, the most important invention is machine learning.

And having seen lots of people do this, I think you as a machine learning researcher, or machine learning developer will be much more productive if you learn to start in prototype, to start in Octave, in some other language.

This is a reasonably commonly use you type machine learning.

And it turns out in machine

Here's the definition of what is machine learning does to Arthur Samuel.

Spectral partitioning, L1 minimization. This comes from the optimization community.

Now, we've seen probabilistic word models before.

We've seen how probability theory can represent in reason with uncertainty.

It's actually quiet amazing how far technology has come in such a short amount of time.

This is the result of computational geometry, gesture recognition, and machine learning.

Our next guest is Vukosi Marivate, who is currently a graduate student in computer science.

Those pieces of junk!

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

Daphne Koller So this is which coin you pick, and this is the two tosses.

So here's an example of that, also from Andrew's Machine Learning class.

But there are a large number of ways you could've done this.

Again, we will talk about those in detail later.

There was a realization that the only way to do these things was to have a machine learn to do it by itself.

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

Now obviously we just scratched the surface on machine learning.

And so gathering that data is much faster, much easier software engineering process than writing this code by hand.

Concretely, if you have a learning algorithm that's not performing as well as you wanted it to, how can you figure out if your learning algorithm is suffering.

You'll also learn about key underlying theory, from information theory, statistics, and machine learning.

So in this class, I teach you some of the very basics of machine learning, and in our next unit

And in the next few videos after that, I'm going to talk about these techniques, which are called the machine learning diagnostics.

There is autonomous robotics, computational biology, tons of things in Silicon Valley that machine learning is having an impact on.