Types of Learning

Supervised

Unsupervised

Supervised

Description:

You give the machine the right answer

How it works:

The machine learns the patterns in the data that caused the observed outcome to occur

Examples:

Housing prices (sale data is available), dating apps (user choice is observed)

Unsupervised

Description:

There are no observable right answers so the machine creates it's own from scratch

How it works:

Generally the machine groups like things together

Examples:

Segmentation (which users are like other users), feature detection in an image

Classes of Problems

Classification

Regression

Clustering

Classification

Description:

The right answer is putting the observation into a category, which can even be a simple yes/no

How it works:

A form of supervised learning where the output is a likelihood of each category occuring

Examples:

Whether a house sold or not (or sold above market, pre-listing, etc.), whether two users liked each other

Regression

Description:

The output is a continuous number (say between 1 and 10,000)

How it works:

Given a set of inputs, the machine predicts what the observed value will be

Examples:

Housing prices (given sq. ft and neighborhood), total sales in June, attendance at an event

Clustering

Description:

Which of my things are like others of my things?

How it works:

The machine groups your observations together into clusters of things that are like each other

Examples:

Grocery store shopper analysis (soccer moms vs. grandparents vs. college kids)