IMT4611 Data Analysis and Statistics or similar level of expertise
Expected learning outcomes
The course offers students a deeper understanding of cutting-edge problems on the theories, methods, and algorithms in machine learning as well as the application of those. Students will strengthen their ability to work with the original scientific literature.
On completion of this course the students will be able to:
- Evaluate and contrast basic techniques and algorithms used in machine learning
- Formulate specific algorithmic requirements for a given problem and propose an appropriate solution
- Predict and judge the performance of a machine learning or data mining method.
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)
Form(s) of Assessment
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 examination.
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.