ITF31519 Practical Machine Learning (Autumn 2025)
Facts about the course
- ECTS Credits:
- 10
- Responsible faculty:
- Faculty of Computer Sciences, Engineering and Economics
- Campus:
- Halden
- Course Leader:
- Lars Vidar Magnusson
- Teaching language:
- Norwegian or English
- Duration:
- ½ year
Table of contents
The course is affiliated to the following study programme
This course is compulsory in
- Bachelor in Computer Science - specialisation in machine learning
Required prerequisite knowledge
Recommended previous knowledge
Teaching semester
Content
Application of
- different techniques for machine learning
- methods for evaluating models for machine learning
The student's learning outcomes on passing the course
KnowledgeThe student
- understands what a machine learning problem is, how to solve it and ethical challenges related to it
- knows the workflow used in machine learning
Skills
- The student can use platforms and packages for machine learning
General competence
- The student can program machines so that they can learn to solve problems on their own
Teaching and learning methods
Workload
Coursework requirements - conditions for taking the exam
Exam
Individual portfolio assignment and individual oral exam
The exam consists of two components:
- Individual portfolio assessment. The students are given an individual tentative grade on the portfolio using the A - F grading scale. This grade can be adjusted up to 2 stages at the oral exam.
- Individual oral exam: Duration approximate 20 minutes. The individual oral exam is based on regular topics in the course and portfolio. No supporting material allowed.
The students will get an individual joint grade from the entire course. Grading scale from A to F.
Examiners
Conditions for resit/rescheduled exams
Upon re-examination, both parts of the exam must be retaken.
New and postponed exams are conducted simultaneously with the next regular exam. The department management may decide to arrange a postponed exam in the following semester for students with valid absence from the regular exam.
Course evaluation
This course is evaluated by a:
- End-of-term evaluation (compulsory)
The responsible for the course compiles a report based on the feedback from the students and his/her own experience with the course. The report is discussed by the study quality committee of the faculty of Computer Sciences.