1. Machine Learning Lucy Hederman

2. KBS Development Stage 1: analysis of the problem that produces a representation of the problem that can be manipulated by the reasoning system - this representation is often a set of attribute values. Stage 2: developing the reasoning mechanism that manipulates the problem representation to produce a solution.

5. Classifying apples and pears To what class does this belong?

12. “ Nearest” - distance/similarity For query q and training set X (described by features F ) compute d ( x,q ) for each x  X, where and where

16. Condensed NN 100 examples 2 categories Different CNN solutions

23. ANN Processing Element (PE) Summation - gives PE’s activation level Transfer function - modifies the activation level to produce a reasonable output value (e.g. 0-1) .

26. Overfitting Training time Error In-sample error Generalisation error Too much training will result in a ( k -NN or ANN) model that makes minimal errors on the training data (memorises), but no longer generalises well. Beware.

27. ANN Development Collect data Separate into training and test sets Define a network structure Select a learning algorithm Set parameters, values, weights Transform data to network inputs Start training, revise weights Stop and test Use the network for new cases. Get more better data Reseparate Redefine structure Select another algorithm Reset Reset