[ML]1. Framing

What is Machine Learning?

ML systems learn how to combine input, to produce useful predictions on never-before-seen data

Terminology

• Label : the vaiable we're predicting, typically represented by the variable y
• Features: input variables describing our data, typically represented by the variables {x1, x2...xn}
• Example : particular instance of data, x
• Labeled example : the example used to train the model, typically represented as (x,y)
• Unlabeled example : the example used for making predictions on new data, typically represented as (x, ?)
• Model : maps examples to predicted labels: y', it is defined by internal parameters, whchi are learned

Regression VS Classfication

A Regression model predicts continuous values.

• What is the value of a house in California?
• What is the probability that a user will click on this ad?

A Classification model predicts discrete values.

• Is a given email message spam or not spam?
• Is this an image of a dog, a cat, or a hamster?