Framing

This module investigates how to frame a task as a machine learning problem, and covers many of the basic vocabulary terms shared across a wide range of machine learning (ML) methods.

Framing

What is (Supervised) Machine Learning?

ML systems learn

how to combine input

to produce useful predictions

on never-before-seen data

Terminology: Labels and Features

Label is the variable we're predicting
- Typically represented by the variable y

Terminology: Labels and Features

Label is the variable we're predicting
- Typically represented by the variable y
Features are input variables describing our data
- Typically represented by the variables {x₁, x₂, ..., x_n}

Terminology: Examples and Models

Example is a particular instance of data, x
Labeled example has {features, label}: (x, y)
- Used to train the model
Unlabeled example has {features, ?}: (x, ?)
- Used for making predictions on new data

Terminology: Examples and Models

Example is a particular instance of data, x
Labeled example has {features, label}: (x, y)
- Used to train the model
Unlabeled example has {features, ?}: (x, ?)
- Used for making predictions on new data
Model maps examples to predicted labels: y'
- Defined by internal parameters, which are learned

Help Center

Introduction to ML (3 min)

Key ML Terminology