Supervised Learning

The machine learns to predict an output when given an input.

Each training case consists of an input and a target output.

Regression

The target ouput takes continuous values.

• Predicting the price of a stock
• Predicting a house price

Classification

The target output is a class label.

• What kind of fruit the input is
• What language a word is

Reinforcement Learning

The machine has to automatically determine the ideal behavior to maximize its performance.

For example:

Using reinforcement learning you can also make a computer program that can complete a Mario level (MarI/O - Machine Learning for Video Games).

Unsupervised Learning

Unsupervised learning allows us to approach problems with little or no idea what our results should look like. We can derive structure from data where we don’t necessarily know the effect of the variables.

The most common type of unsupervised learning is cluster analysis or clustering. It is the task of grouping a set of objects in such a way that object in the same group (cluster) are more similar to each other than to those in other groups.

There is also non-clustering unsupervised learning. An example thereof is identifying individual voices and music from a mesh of sounds. This is called the “Cocktail Party Algorithm”.