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
My blogs: AI club, Quantum club
