Student Pages
Undergraduate course

Machine Learning

Main content

Level of Study


Teaching semester


Objectives and Content

The course introduces Machine Learning, with a view towards data analysis applications. Topics covered are supervised learning (classification and regression) with deep learning, unsupervised learning including clustering, reinforcement learning, and the practice of machine learning.

Learning Outcomes

A student who has completed the course should have the following learning outcomes defined in terms of knowledge, skills and general competence:


The candidate

  • has theoretical knowledge about the principles of machine learning
  • has a basic understanding of the contemporary machine learning algorithms
  • has a broad knowledge about the use of machine learning in data analysis, its advantages and limitations


The candidate

  • can analyze and design machine learning solutions for data analysis applications

Required Previous Knowledge

INFO132 or equivalent. Basic understanding of programming and algorithms.

Recommended Previous Knowledge

INFO132 or equivalent. Basic understanding of programming and algorithms.

Credit Reduction due to Course Overlap

INF264 (10 ECTS)

Access to the Course

The course is open to students with admission to study at the UiB. The course has 200 study places. Students who have this course as a compulsory part of their study plan will have priority access.

Teaching and learning methods

Lectures, seminars and data labs, normally 2 + 2 hours per week for 12-15 weeks.

Compulsory Assignments and Attendance

Participation: compulsory attendance at labs (at least 75%).

Approved compulsory requirements are valid for the two following semesters.

Forms of Assessment

  • Group assignment where students demonstrate their ability to analyze and design machine learning solutions (30% of grade)
  • 2 hour digital home exam (70% of grade) - multiple choice exam

Examination Support Material

All written material in paper form is allowed on the exam.

Grading Scale

The grading system has a descending scale from A to E for passes and F for fail.

Assessment Semester

Assessment only in teaching semester.

Course Evaluation

All courses are evaluated according to UiB's system for quality assurance of education.



Telephone 55 58 90 00

Exam information

  • Type of assessment: Group assignment and home exam

    Withdrawal deadline
    • Exam part: Group assignment

      Submission deadline
      03.05.2023, 14:00
      Examination system
      Digital exam
    • Exam part: Multiple choice exam

      Assignment handed out
      22.05.2023, 09:00
      Submission deadline
      22.05.2023, 11:00
      Examination system
      Digital exam