A Practical Definition of Machine Learning

A practical definition of machine learning is to use data to compute hypothesis g that approximates target f.

Take credit approval as an example

input x 𝑿 (customer application) output y 𝒀 (good/bad after approving credit card)

unknown pattern to be learned ⟺ target function 𝑓: 𝑿 → 𝒀 is a ideal credit approval formula

historical records in bank

data ⟺ training examples

𝐷 = {(𝒙₁, 𝒚₁),(𝒙₂, 𝒚₂), …, (𝒙ₙ,𝒚ₙ)}

hypothesis ⟺ skill with hopefully good performance 𝑔: 𝑿 → 𝒀 is a ‘learned’ formula to be used

{(𝒙ₙ,𝒚ₙ)} → Machine Learning → 𝑔

The learning model

assume 𝑔 𝐻 = { 𝒉ₖ }

i.e. approving if

𝒉₁ : annual salary > $10,000

𝒉₂ : debt > $1,000

hypothesis set 𝐻

can contain good or bad hypotheses

up to 𝐴 to pick the ‘best’ one as 𝑔

learning model = 𝐴 and 𝐻

I hope that this information will help. If you need any further information, please feel free to contact me. https://discord.gg/qHj8sHrctS

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