When we fit the parameters of our learning algorithm we think about choosing the parameters to minimize the training error.

Here's one thing you could do you could take your first model and minimize the training error and this would give you some parameter vector theta, and you can then take your second model, the quadratic function and for that your training set and this will give you some other parameters vector theta.

First, you learn the parameters theta from the training set so you minimize the usual training error objective j of theta, where j of theta here was defined using that 70 of all the data you have.

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All thanks to your training, Master

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So we think of Ci as picking the cluster centroid with the smallest squared distance to my training example Xi.

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Of course you need to be in physical, very good, condition, so I'm training a lot.

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That distance is called the margin, when I draw up this two extra blue lines, we see that the black decision boundary has some larger minimum distance from any of my training examples, whereas the magenta and the green lines they come awfully close to the training examples.

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