Machine Learning

Undergraduate course

Course description

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

Level of Study


Semester of Instruction

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 UiB.

The course has 200 study places and enrolment is based on application in StudentWeb.

Students who have this course as a compulsory part of their study plan will have priority access.

The application deadline is Monday week 2.

You will receive confirmation of whether you received a seat no later than Thursday the same week as the deadline.

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 not valid beyond the semester when they are approved.

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
  • The exam assignment will be given in the language of instruction in the course.
    The exam answer must be submitted in the same language as the exam assignment.

    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.

    Students with valid absence as defined in the UiB regulations ยง 5-5 can apply for an extended submission deadline to The application must be submitted before the deadline for submission has expired.

    Course Evaluation
    All courses are evaluated according to UiB's system for quality assurance of education.
    Examination Support Material
    All written material in paper form is allowed on the exam.