Forventet læringsutbytte

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

Emnets temaer

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

Pedagogiske metoder

Forelesninger
Gruppearbeid
Lab.øvelser
Oppgaveløsning
Annet

Pedagogiske metoder (fritekst)

Annet - homework

Vurderingsformer

Skriftlig eksamen, 3 timer
Annet

Vurderingsformer

* Written exam, 3 hours (60%)

* Homework evaluation (4x10%)

All parts must be passed.

Karakterskala

Bokstavkarakterer, A (best) - F (ikke bestått)

Sensorordning

Evaluated by the lecturer(s)

Gjennomføring av kontinuasjon

The whole course must be repeated.

Tillatte hjelpemidler (gjelder kun skriftlig eksamen)

Approved calculator

Obligatoriske arbeidskrav

None.

Læremidler

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