Download IMT4631 Machine Learning and Data Mining

IMT4631 Machine Learning and Data Mining Course code:
IMT4631
Course name:
Machine Learning and Data Mining
Course level:
Master (syklus 2)
ECTS Credits:
5
Duration:
Vår
Duration (additional text):
First half of spring semester
Language of instruction:
English
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.
Topic(s):
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:
Lectures
Group works
Laboratory work
Exercises
Other
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Teaching Methods (additional text):
Annet - homework
Form(s) of Assessment:
Written exam, 3 hours
Other
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:
The whole course must be repeated.
Tillatte hjelpemidler:
Examination support:
Approved calculator
Coursework Requirements:
None.
Academic responsibility:
Faculty of Computer Science and Media Technology
Course responsibility:
Associated professor Katrin Franke
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
Publish:
Yes
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