Summary

Perceptron training rule guaranteed to succeed if

• Training examples are linearly separable
• Sufficiently small learning rate $\eta$

Linear unit training rule uses gradient descent

• Guaranteed to converge to hypothesis with minimum squared error
• Given sufficiently small learning rate $\eta$
• Even when training data contains noise
• Even when training data not separable by H