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 (18/19 Feb)

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

Week #2 (25/26 Feb)

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

Week #3 (04/05 Mar)

• Reporting in R
• Data Import in R
• Data Pre-Processing in R (Solutions)

Week #4 (11/12 Mar)

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

Week #5 (18/19 Mar)

• Predictive Modelling (1/3) Wine dataset

Week #6 (25/26 Mar)

• Predictive Modelling (2/3)

Week #7 (01 Apr)

• Predictive Modelling (3/3)

Week #8 (08/09 Apr)

• Performance Estimation

Week #9 (06/07 May)

• Handling Big Data in R

Week #10 (13/14 May)

• Imbalanced Domain Learning (1/2)

Week #12 (20/21 May)

• Imbalanced Domain Learning (2/2)

Week #13 (27/28 May)

• Explainability

Week #14 (03/04 Jun)

• Practical Assignment Evaluation