- Supervised machine learning is ML systems learn how to combine input to produce useful predictions on never-before-seen data
- Label is a variable we are predicting, typically represented by the variable 'y'
- Features are input variables describing the data, typically represented by the variables {x1, x2, x3,..., xn}
- 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 prediction on new data
- Model maps examples to predicted labels: y', defined by internal parameters, which are learned