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
