Review Questions on Machine Learning

This is a voluntary quiz to help you decide whether you have understood some of the major concepts from this topic. It carries no marks.

What do dendrites, axon, and synapses, in a biological neuron, correspond to in the artificial neuron model described in lectures?
Why is a non-linearity used in the artificial neuron model described in lectures? What are the important features of a suitable non-linearity?
What is the weight change equation used by the error backpropagation learning algorithm, and what do the symbols in this equation signify?
What happens in the forward pass in error backpropagation learning? What happens in the backward pass in error backpropagation learning?
What does over-fitting mean in the context of error backpropagation learning?
What is a decision tree?
Give the formula for the entropy measure used in lectures.
Briefly, in words, describe how ID3 chooses the best attribute to split on.
Give two reasons why there might be "noise" in training data.
What is the formula for the Laplace error estimate?

Solutions: don't read until you've tried the questions yourself!