Perceptron Learning
	• Perceptron learning is a gradient descent search through the space of possible weights. • Each training example provides an "error surface" for weights.  Learning rule runs weights downhill with learning rate a as step size. • For linearly separable functions, there are no local minima, and guaranteed to converge if learning rate a not too high (overshoot) • Summary: Very effective for very simple representable functions.