What is Machine Learning
Defenition:
Improving some performance measure with experience computed from data.
An alternative route to build complicated systems.
Key Essence:
- exists some ‘underlying pattern’ to be learned
- no programmable (easy) definition
- somehow there is data about the pattern
Key Essence help decide whether to use ML.
Components of ML
Basic Notations:
- input: x ∈ X
- output: y ∈ Y
- unknown pattern to be learned ⇔ target function: f : X → Y
- data ⇔ training examples: D = {(
,
),(
,
),··· ,(
,
)}
- hypothesis ⇔ skill with hopefully good performance: g: X → Y
use data to compute hypothesis g that approximates target f