Machine Learning and Data Mining
2009-2010 - IMT4631 - 5 ECTS

Expected learning outcomes

The course offers students a deeper understanding of the theories, methods, and algorithm in machine learning as well as the application of those.


1. Symbolic Learning

2. Statistical Learning

3. Artificial Neural Networks

4. Support Vector Machines

5. Cluster Analysis

6. Fuzzy Logic

7. Evolutionary Computation

8. Hybrid Intelligent Methods

Teaching Methods

Group works
Laboratory work

Teaching Methods (additional text)

Annet - homework

Form(s) of Assessment

Written exam, 3 hours

Form(s) of Assessment (additional text)

* Written exam, 3 hours (60%)

* Homework evaluation (4x10%)

All parts must be passed.

Grading Scale

Alphabetical Scale, A(best) – F (fail)

External/internal examiner

Evaluated by the lecturer(s)

Re-sit examination

For the written exam: Ordinary re-sit examnination.

Examination support

Approved calculator

Coursework Requirements


Teaching Materials

Basic Textbook: Machine Learning and Data Mining: Introduction to Principles
and Algorithms (Paperback) by Igor Kononenko (Author), Matjaz Kukar (Author)
+ selected research papers

Additional Literature for interested readers:
Pattern Recognition and Machine Learning (Information Science and
Statistics) by Christopher M. Bishop
Pattern Classification (2nd Edition) by Richard O. Duda, Peter E. Hart, and
David G. Stork
Machine Learning by Tom M. Mitchell

Additional information

In case there will be less than 5 students that will apply for the course, it will be at the discretion of Studieprogramansvarlig whether the course will be offered or not an if yes, in which form.