Materials

WEKA tool

The course will provide hands-on exercises with the WEKA workbench, which can be downloaded here. If you don't have administrator rights on your system, you can also download a single JAR file.

Reading material

The accompanying book is:

• I. Witten, E. Frank, M. Hall. Data Mining: Practical Machine Learning Tools and Techniques (3rd Edition), Morgan Kaufmann, 2011.

This book is included in the registration fee.

For who is interested in a business point of view, we recommend:

• F. Provost, T. Fawcett. Data Science for Business, O'Reilly, 2013.

A copy of this book will be made available during the course to browse through.

Datasets

• Session on Getting to Know Your Data: eda.arff
• Session on Prediction: labor.arff, contact-lenses.arff, segnoise.arff
• Session on Data Engineering: gene_expression.arff
• Session on Probabilistic Models: credit_train.arff, smoking.arff, smoking.xml
• Session on Clustering/Association Rule Mining: iris.arff, breast-cancer.arff
• Challenge:
  • Datasets:
    • walking_train.arff
    • walking_test.arff
    • Original sensor measurements + full dataset: walking.zip
  • The MotionFingerprint tool to use: JAR file and README

IPython notebook

For those more familiar with Python, we also have an IPython notebook available for all the exercises done in this course:
Download DSIPy