FIT5047 – Intelligent Systems Week 10

Week 10 moved on from classification to clustering. Although, conceptually, there was close relation to topics covered in Natural Computation the methods discussed were new. Again, Euclidean distance is a fundamental measure of similarity/uniquness. The first method introduced was Heirarchical Clustering. This introduction was very bried and reference to the text would need to be made for issues such as linkages. The next method was K-Means clustering.      .. Read More

FIT5167 – Natural Computation Week 10

Time series forecasting was the topic of week 10’s lecture. To complete time series forecasting we first need to remove anything that is easy to forecast from the data: Trends Cycles (Cyclical Components) Seasonal variations ‘Irregular’ the hardest to predict and the component which our neural networks will be attempting to forecast. Autocorrelation generally stronger for recent data items and degrades in quality as we step back through the time.. Read More