minimize more complicated loss function

Gradient
L = Σj(yj – w1x0 – w0)2 ->min
ΘL/w1 = -2Σj(yj-w1xj-w0)xj
ΘL/w0 = -2Σj(yj-w1xj-w0)

Perception algorithm
Linear seperator
w1x + w0 >= 0
0 if w1x + w0 < 0 Linear function Linear Method -regression vs classification -exact solution vs iterative solution -smoothing -non-linear problems Supervised Learning -> parametic

KNN definition
learning: memorize all data

Problems of KNN
-very large data set
kdd trees
-very large feature spaces