Knowledge Extraction and Machine Learning

Lecturer: José Luís Borges   Office: L215

• Classes' tentative plan
• Project:
  • Progress report guidelines
  • Final report guidelines

Topics Covered

• Introduction to Data Mining

• Descriptive Statistics (lab: week 02 - excel)

• Data Preparation (lab: week 03 - spss  week 03 - Risk.csv  )

• Association Rules (lab: week 04 - R  week 04 - dataset_california.txt  week 04 - 1R_exemplo.txt )

• Clustering (lab: week 05 - weka  week 05 - bank-data.csv )

• Clusering - parte II (lab: week 06 - lab week 06 - nutrient week 06 - Risk)

• Classification (lab: week 07 - lab  week 07 - datasets)

• Decision Trees (lab: week 08 - lab week 08 - datasets)

• Decision Trees Prunning, Rules and Evaluation (lab: week 09 - lab  week 09 - datasets)

• Neural Networks and Ensemble Classifiers (lab: week 10 - lab  week 10 - datasets)

• Web and Text Mining

Software

• Weka: Data Mining Software in Java
• SPSS statistics 17
• R: The R Project for Statistical Computing
• RapidMiner
• Excel 2010

Other editions

• 2009/2010
• 2008/2009
• 2007/2008
• 2006/2007
• 2005/2006
• 2004/2005