ITF31519 Practical Machine Learning (Autumn 2022)

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

Elective course for others.

Required prerequisite knowledge

ITF10619 Programmering 2

Recommended previous knowledge

ITD20218 Statistikk og statistisk programmering and ITD15020 Kalkulus (or taken in parallel with this course)

Teaching semester

5th semester (Autumn).

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

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

Exam

Individual portfolio assignment and individual oral exam

The exam consists of two components: 

  1. 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.
  2. 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.

Reading list

The current reading list for AUTUMN 2022 can be found in Leganto.
Last updated from FS (Felles studentsystem) Oct. 15, 2026 3:02:21 AM