ANU Data Mining Group

Data Mining Course 2006



Lecture and lab times and venues

Course coordinator

John Maindonald

We don't actually have formal prerequisites. We assume that participants have basic computing, statistical and mathematical skills with honours level expertise in either of these areas. Our intended audience are honours and graduate students in these areas. We intend to look at both mathematical (algorithm) and computing issues.

In the course we will analyse algorithms, do some coding and look at efficiency issues, and also do some hands on data analysis (data mining).


Course Assessments

The assessment for this year's course is as follows:

  1. Four assignments, two worth 10% and two worth 15%.
  2. Paper presentation worth 20% (in weeks 12 and 13).
  3. Examination (take home or written) worth 20%.
  4. The final course mark will be the sum of the assignments, presentation and exam marks.

Course Material

Lab material is available from John's MATH3346 lab Web site.

Documentation on Rattle (Graham's lab): rattle.pdf.


Last update: Peter Christen, 26 September 2006