Linear regression

Housing prices (size in feet => price in USD)

• m - number of examples in the dataset

• X's - input variables, features

• y's - output variables, target variables

• (X, y) - single training example

• (Xi, yi) - i-th training example

• Training set => Learning Algorithm => h (hypothesis)

• is function that converts X to estimated y. y = h(X) as it is a linear function we can also write h(x) = ax^2 + b (a, b could be theta 0 and 1)

• Linear regression with one variable (aka.) Univariate Linear regression.