Data Mining and Knowledge Discovery

Lectures:

Recommended for:
It applies to other graduate schools, as well.

The course last ran:
Fall 97.

Goals:
To overview methodologies suitable for the data exploration supported by the examples.

Prerequisites:
None.

Organization:
Mainly lectures, seminar presentations in addition.

Contents:
Quantitative and qualitative data analyses. Relating expectations of the user domains to a corresponding level of information/knowledge engineering. Addressing questions of time granularities. Estimating the scope and performance of data mining and knowledge discovery.

Literature:
To be defined later.

Teachers:
Ankica Babic.

Examiner:
Active participation, seminar presentations.

Schedule:
To be defined.

Examination:
Ankica Babic.

Credit:
3 credits.

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