Machine learning algorithms can primarily be of two types:

• Supervised learning: In this type of learning, we are given an input dataset along with the correct labels, and we need to learn the the relationship (as a function) between the input and the output. The handwritten-digit classification problem is an example of a supervised (classification) problem.
• Unsupervised learning: In this type of learning, we have little or no idea what our output should look like. We can derive structure from data where we don't necessarily know the effect of the variables. An example is clustering, which can also be thought of as segmentation, in image processing technique where we do not have any prior knowledge of which pixel belongs to which segment.

A computer program is said to learn from experience, E, with respect to some task, T, and some performance measure, P, if its performance on T, as measured by P, improves with experience, E.

For example, let's say that we are given a set of handwritten digit images along with their labels (digits from zero to nine) and we need to write a Python program that learns the association between the images and labels (as experience E) and then automatically labels a set of new handwritten digit images.

In this case, the task, T, is the assignment of labels to the image (that is, classifying or identifying the digit image). The proportion of the set of new images correctly identified will be the performance, P, (accuracy). of the program. In this case, the program can be said to be a learning program.

In this chapter, we will describe a few image processing problems that can be solved using machine learning algorithms (unsupervised or supervised). We will start with learning the applications of a couple of unsupervised machine learning techniques in solving image processing problems.