ITF31519 Practical Machine Learning (Autumn 2022)
Facts about the course
- ECTS Credits:
- 10
- Responsible department:
- Faculty of Computer Science, Engineering and Economics
- Campus:
- Halden
- Course Leader:
- Lars Vidar Magnusson
- Teaching language:
- Norwegian or English
- Duration:
- ½ year
The course is connected to the following study programs
This course is compulsory in
Bachelor in Computer Science - specialisation in machine learning
Elective course for others.
Absolute requirements
ITF10619 Programmering 2
Recommended requirements
ITD20218 Statistikk og statistisk programmering and ITD15020 Kalkulus (or taken in parallel with this course)
Lecture Semester
5th semester (Autumn).
The student's learning outcomes after completing 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
Content
Application of
different techniques for machine learning
methods for evaluating models for machine learning
Forms of teaching and learning
Lectures, project work and lab-supervision.
Workload
Approx. 250 hours.
Coursework requirements - conditions for taking the exam
Up to 4 mandatory exercises. The coursework requirements must be approved before the student can take the exam
Examination
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
External and internal examiner, or two internal examiners.
Conditions for resit/rescheduled exams
Upon re-examination, both parts of the exam must be retaken.
Course evaluation
This course is evaluated by a:
Mid-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.
Literature
The current reading list for AUTUMN 2022 can be found in Leganto.