So in this course, we are going to discuss the out of box machine learning technique called support
vector machines.
And the beginning of the course, we will discuss maximal marginal, classify this maximal margin,
classify it is a very simple and intuitive classifier and support vector machine is a generalization
of this classifier only.
However, this classifier can only be applied when classes are linearly separable.
Therefore, the application of this classifier are severely limited.
Next, we will discuss support recook classifier, which is an extension of the maximal marginal classifier
and can be applied in a broader range of cases.
Lastly, we will cover support vector machines, which is a further extension of the support vector
classifier.
We will see that how SBM accommodate non-linear class boundaries.
Usually Astrium is meant to handle binary classification, but later on we will also see how to extend
support vector machines to the case of more than two classes.
Oftentimes these three terms are used interchangeably, but there is a difference between these three
types of classifiers and we will note what is the difference and how these three are different.