Minimize quadratic loss
minΣ(yi-w1xi-w0)2 = L
ΘL/Θw0
Σxiyi – 1/m ΣyiΣxi – w/m(Σxi)2 = w1Σxi2
f(x)= w1X + w0
w0 = 3
w1 = -1
Sum(x_i y_i) – (1/M) Sum(y_i) Sum(x_i) + (w_1/M)( Sum(x_i) )^2 = w_1 Sum(x_i^2)
Regularization
loss = loss(data)+loss(parameters)
Σj(yi-wixi-w0)^2 + Σi|wi|p
