Course Description

This is a practical introduction to data mining using the Weka machine learning workbench. It is aimed at end users who are new to Weka. Please bring a laptop on which you have pre-installed Weka 3.6. Weka runs on Windows, Mac and Linux. You will also need to download this dataset.

Lecture material will be interspersed with practical exercises. I will introduce basic concepts such as evaluation and overfitting. I will describe simple machine learning methods, including statistical modeling, decision trees and rules, association rules, various kinds of linear models, instance-based learning and clustering.

I will get you started with the main features of the Weka Explorer, including filters, classifiers, and visualization. Then you will work through some simple exercises in tutorial style. These include nearest neighbor learning, decision trees, visualization classification boundaries, preprocessing and parameter tuning, document classification, and mining association rules.

Presenter

Professor Ian H. Witten

Presenter Biography

Ian H. Witten is Professor of Computer Science at the University of Waikato in New Zealand where he directs the New Zealand Digital Library research project. His research interests include language learning, information retrieval, and machine learning. He has published widely, including several books, such as Managing Gigabytes (1999), How to build a digital library (2003), Data Mining (2005) and Web Dragons (2007). He is a Fellow of the ACM and of the Royal Society of New Zealand. He received the 2004 IFIP Namur Award, a biennial honour accorded for "outstanding contribution with international impact to the awareness of social implications of information and communication technology" and (with the rest of the Weka team) the 2005 SIGKDD Service Award for "an outstanding contribution to the data mining field" and in 2006 the Royal Society of New Zealand Hector Medal for "an outstanding contribution to the advancement of the mathematical and information sciences."