Supervised Learning000

• the process of supervised learning

  given dataset with labels then learn the hypothesis/predict function h: x->y through learning algorithm. When the target variable we try to predict is continuous, we call it regression problem. Whrn the target variable we try to predict is discrete, we call it classification problem.

•  Linear regression

e.g. prediction of houses' price, give living area x₁, number of bedrooms x₂

h_θ(x) = θ₀ + θ₁x₁ + θ₂x₂ 

θi's are parameters/weight value, for simplicity, we can write in vectors as

h(x) = sum(θ_ix_i )  = θ^Tx

to get parameter θ we define the cost function:

J(θ) = 1/2sum(h_θ(x⁽ⁱ⁾) - y⁽ⁱ⁾)²

• • LMS algorithm

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