The list of obligatory and relevant courses is given in Table 1. Each course is lectured once per year and the students have the freedom to take them when they wish.
Normal courses last half a year with two hours of lectures and two hours of exercise sessions per week. Attendance to these is not obligatory. The course is completed by taking an examination and submitting a project assignment. The project assignments typically involve programming and analysing a given data set.
Special courses are given as seminars with a varying topic each semester. The organisational details of these courses vary but typically the organiser of the seminar gives an introductory lecture and has a list of topics for presentations. Each student gives one or two seminar presentations and possibly selects a problem for each topic. The special course is completed by attending a certain percentage of sessions, giving the required amount of presentations, and by submitting written solutions to problems. On some courses, there can also be one or several project works. Usually there is no examination.
The text books used in the obligatory courses are as follows. The two courses on machine learning are based on [Alpaydin, 2004] and [Bishop, 2006], machine learning and neural networks is based on [Ham & Kostanic, 2001] and other material. The data mining course uses [Mannila & Toivonen, 1998,Hand et al., 2001]. Information visualisation course is based on [Ware, 2004].