Machine Learning and Data Mining
- 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 (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.
Alphabetical Scale, A(best) – F (fail)
Evaluated by the lecturer(s)
For the written exam: Ordinary re-sit examnination.
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
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.