IKT112 Concepts of Machine Learning
- ECTS Credits:
- 5
- Responsible department:
- Faculty of Engineering and Science
- Course Leader:
- Christian Walter Peter Omlin
- Lecture Semester:
- Spring
- Teaching language:
- English
- Duration:
- 1 term
The course is connected to the following study programs
- Artificial Intelligence, 5-year master programme
Teaching language
EnglishCourse 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.)
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
Teaching 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.
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.
Assessment methods and criteria
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.
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 |