alleryStart learning →The resulting per-example log-likelihood is, up to constants
AProportional to −(y⁽ⁱ⁾ − θᵀx⁽ⁱ⁾)²
BProportional to |y⁽ⁱ⁾|
CIndependent of θ
DProportional to log(θᵀx⁽ⁱ⁾)
Answer & Solution
Correct answer: A. Proportional to −(y⁽ⁱ⁾ − θᵀx⁽ⁱ⁾)²
log p(y|x; θ, σ) under Gaussian noise = -1/(2σ²)·(y - θᵀx)² + const.
Maximising the log-likelihood w.r.t.
θ is equivalent to minimising the squared-error sum — derives least-squares as MLE.
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