Classes

In order to allow the students to prepare for the upcoming classes, the respective materials will be updated the day before each class.

Week #1 (4 Mar)

• Presentation
• Intro to Data Mining
• Intro to R (and RStudio)
• Basic Concepts in R (1/2)

Week #2 (11 Mar)

• Basic Concepts in R (2/2) (Solutions)

Week #3 (18 Mar)

• Reporting in R
• Data Import in R
• Data Pre-Processing in R

Week #4 (25 Mar)

• Data Summarisation in R (Solutions)
• Data Visualisation in R (Solutions)

Week #5 (1 Apr)

• Predictive Modelling (1/3) Wine dataset
  • Hands-on Decision Trees
  • Hands-on Naive Bayes

Week #6 (8 Apr)

• Predictive Modelling (2/3)
  • Hands-on KNN
  • Hands-on SVM

Week #7 (22 Apr)

• Predictive Modelling (3/3)
  • Hands-on Ensembles
  • Hands-on Clustering

Week #8 (29 Apr)

• Performance Estimation
• Handling Big Data in R

Week #9 (13 May)

• Theoretical Test!

Week #10 (20 May)

• Theoretical Test (improvement)
• Imbalanced Domain Learning (1/2)

Week #11 (27 May)

• Imbalanced Domain Learning (2/2)

Week #12 (3 Jun)

• Practical Assignment Q&A