Day 2 of the Machine Learning MSc module at Manchester saw us learning about Decision Trees and the role that entropy, linear correlation and mutual information can play.
It’s all about categorical data (like name, a set of fixed values), whereas last week was about the automated classification of continuous data (like temperature, a smooth range of values). The algorithms we were looking at to automatically build decision trees using the inherent statistical and probabilistic properties of a set of data to try and maximise the decision accuracy with the minimum overhead of computation and memory.
Today’s stuff didn’t seem too tricky, and last week’s lab assessment went pretty well.
This week, we need to use the mi() and h() fuctions from the a Matlab Mutual Information library here. Sounds great, but – I’m getting problems using it referring to undefined symbols that may be related to the 64-bit OS on this machine, so I’ll need to try a few options to work around that. Need to get that working!
Well, it’s been a long day so I’ll call a close here. Cheers!