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

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

Welcome to the machine learning unit.

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.

Let's consider the problem of computer vision.

One final note

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

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

This time it's about cancer testing.

Machine learning algorithms for data mining tasks

We had so many motors, you know, aligning the joints, and those kinds of things.

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.

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

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.

So, we can construct methodologies that elicit these models from a human expert, or ones that learn it from historical data using statistical machine learning techniques, or a combination of the two.

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

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

So congratulations.

Peter will tell you some more about machine learning.

Training Statistics

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

So it turns out that in most of math, the one index version is more common For a lot of machine learning applications, zero index vectors gives us a more convenient notation.

So that's what we're going to focus on.

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.

But just be aware that when we are talking about machine learning applications, sometimes I will explicitly say when we need to switch to, when we need to use the zero index vectors as well.

And to do that, I'm going to tell you about a framework which is very popular in statistics and machine learning of the last 50 years called Bayesian decision theory.

And there's another in joke among statisticians, and that's,

We had knowledge about the electromechanical efficiency and then we can calculate the aerodynamic efficiency.

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.

Silicon Valley and around the world.

Here are some other examples of machine learning. There's database mining.

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