Machine Learning Cheat Sheet Part 1 - Supervised and Unsupervised Machine Learning

1. Machine Learning definition:

Field of study that gives computer the ability to learn without being explicitly programmed.

Arthur Samuel (1959)

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.

Tom Mitchel (1998)

2. Supervised Machine Learning

Supervised machine learning is the task of inferring a function from labeled training data. The training data consist of a set of training examples. Each example is a pair consisting of an input value/s (usually a vector) and a known output value. A supervised learning algorithm analyses the training data and produces an inferred function, which can be used for mapping new examples. An optimal scenario will allow for the algorithm to correctly labels unseen instances.

The inferred function could represent:

• regression - predict continuous valued output, or
• classification or categorisation - predict discrete valued output, for example, 0 and 1.

3. Unsupervised Machine Learning

Unsupervised machine learning is the task of inferring a function to describe hidden structure from unlabelled data (see Cocktail Party Effect).