Table of contents

The course is connected to the following study programs

  • Artificial Intelligence, 5-year master programme

Teaching language

English

Learning outcomes

Upon completion of this course, students will have:

  • a basic understanding of the concepts of machine learning including supervised, unsupervised and reinforcement learning
  • the ability to apply machine learning algorithms to data sets using a software package (e.g. WEKA)
  • the ability to design experiments and to empirically evaluate and compare the performances of machine learning algorithms
  • an appreciation of and familiarity with some current real-world applications of machine learning

Contents

  • Supervised learning: decision trees, artificial neural networks, Bayesian learning
  • Unsupervised learning: K-means clustering, hierarchical clustering, principal components
  • Introduction to reinforcement learning
  • Real-world applications of machine learning (e.g. self-driving cars, medical imaging, natural language processing, etc.)

Teaching and learning methods

The course is organized with a combination of lectures, assignments, paper studies, labs, and report writing. The tasks are done individually or in small groups with group supervision. The workload for the average student is approximately 135 hours.

Examinations

3 hours written exam (50%). Portfolio assessment (50%). Information about the content of the portfolio will be given in Canvas at the start of the semester for each seminar. Graded assessment.

Student evaluation

The person responsible for the course decides, in cooperation with student representative, the form of student evaluation and whether the course is to have a midway or end of course evaluation in accordance with the quality system for education, chapter 4.1.

Offered as a free-standing course

Yes. Subject to availability or capacity.

Reduction of Credits

This course’s contents overlap with the following courses. A reduction of credits will occur if one of these courses is taken in addition:

Course Reduction of Credits
IKT109 – Principles of Artificial Intelligence. 5
Last updated from FS (Felles studentsystem) Oct. 15, 2026 2:54:48 PM