1. Machine Learning (ML) with Weka Weka can classify data or approximate functions: choice of many algorithms

2. Weka: can use GUI or command line Click Explorer

3. Data Preprocessing Open.arff file: click "Open file", browse to file

4. The well-known iris ML data set For sepallength attribute we see distribution of classes (colors). We see min, max, mean, standard deviation of numeric attribute.

5. Data Preprocessing Click Edit to view/modify the.arff file

6. Data Preprocessing Click Filter Choose. Select filters > unsupervised > Instance > Randomize Press Apply. Click Edit: see that examples are now randomized

7. Data Preprocessing Click Filter Choose. Select filters > unsupervised > Instance > Normalize Press Apply. Click Edit: see that examples are now normalized also. Can save

8. Data Preprocessing Statistics have changed due to data normalization

9. Select Attributes if too many Choose Principal Components (will select Ranker also) 0.517 petallength + 0.512 petalwidth - 0.492 sepalwidth - 0.478 sepallength -0.747 sepallength + 0.626 sepalwidth - 0.198 petallength + 0.104 petalwidth Alternative: Info gain attribute evaluation (will select Ranker also)

10. Classify Click Classify, Choose: functions > Multilayer Perceptron Left-click for properties, change GUI to true, press start to see ANN topology 4 green input nodes(1 per attribute), 3 red hidden nodes {user controlled: defaults to a=(inputs +outputs) / 2}, 3 yellow output nodes (1 per class)

11. Data Preprocessing Click Open file

12. Data Preprocessing Click Open file

13. Data Preprocessing Click Open file