Supervised versus unsupervised learning
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...
