These are my notes for unit 6 of the AI class.

Unsupervised Learning

So welcome to the class on unsupervised learning. We talked a lot about supervised learning in which we are given data and target labels. In unsupervised learning we're just given data. So here is a data matrix of data items of n features each. There's m in total.

So the task of unsupervised learning is to find structure in data of this type. To illustrate why this is an interesting problem let me start with a quiz. Suppose we have two feature values. One over here, and one over here, and our data looks as follows. Even though we haven't been told anything in unsupervised learning, I'd like to quiz your intuition on the following two questions: First, is there structure? Or put differently do you think there's something to be learned about data like this, or is it entirely random? And second, to narrow this down, it feels that there are clusters of data the way I do it. So how many clusters can you see? And I give you a could of choices, 1, 2, 3, 4, or none.

The answer to the first question is yes, there is structure. Obviously these data seem not to be completely randomly determinate. There seem to be, for me, two clusters. So the correct answer for the second question is 2. There's a cluster over here, and there's a cluster over here. So one of the tasks of unsupervised learning will be to recover the number of clusters, and the centre of these clusters, and the variance of these clusters in data of the type I've just shown you.

AI class unit 6

Unsupervised Learning