This week is about Support Vector Machine (SVM).

First we will learn about Large Margin Classification, in reference to the larger minimum distance from any of the training samples.

We will study Kernels, and the adaptation to non-linear classifiers.

Choosing landmarks will also be covered.

C parameter will be studied.

Similarity and Gaussian Kernels are also main keywords of this session.

We will get good advice about using SVM vs Logistic Regression vs Neural Networks.