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, ..., xn}
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
