Student Pages
Undergraduate course

Introduction to Machine Learning

  • ECTS credits10
  • Teaching semesterAutumn
  • Course codeINF264
  • Number of semesters1
  • LanguageEnglish
  • Resources

Main content



Teaching semester


Objectives and Content

Machine learning is a branch of artificial intelligence focusing on algorithms that enable computers to learn from and change behavior based on empirical data. The course gives an understanding of the theoretical basis for machine learning and a set of concrete algorithms including decision tree learning, artificial neural networks, Bayesian learning, and support vector machines. The course also includes programming and use of machine learning algorithms on real-world data sets.

Learning Outcomes

On completion of the course the student should have the following learning outcomes defined in terms of knowledge, skills and general competence:


At the end of the course the student should:

  • understand the basic ideas of machine learning
  • be able to compare modeling aspects of various machine learning approaches


At the end of the course the student should:

  • develop and implement machine learning algorithms
  • apply and evaluate machine learning algorithms on real data sets

General competence

At the end of the course the student should:

  • have a good overview of how machine learning can be used in different contexts in the society

Required Previous Knowledge

For incoming exchange students: At least 60 ECTS in Computer Science and at least 10 ECTS in mathematics

Recommended Previous Knowledge

Programming skills are recommended. Basic knowledge on data structures (e.g., INF102 or INFO135) is useful. Good mathematical background, especially in linear algebra (e.g. MAT121), calculus (e.g. MAT101, MAT105 or MAT111) and probability (e.g., STAT101 or STAT110).

Credit Reduction due to Course Overlap

INF283, INFO284, 10sp.

Access to the Course

Access to the course requires admission to a programme of study at The Faculty of Mathematics and Natural Sciences

Teaching and learning methods

Lectures, max. 4 hours per week

Exercises, 2 hours per week

Independent projects

Compulsory Assignments and Attendance

Compulsory assignments are valid for one subsequent semester.

Forms of Assessment

Written exam (3 hrs). The compulsory exercises can be graded and this grade can count for the final grade. Both the exam and the compulsory exercises must be passed.

Examination Support Material


Grading Scale

The grading scale used is A to F. Grade A is the highest passing grade in the grading scale, grade F is a fail.

Assessment Semester

Examination both spring semester and autumn semester. In semesters without teaching the examination will be arranged at the beginning of the semester.

Reading List

The reading list will be available within June 1st for the autumn semester and December 1st for the spring semester

Course Evaluation

The course will be evaluated by the students in accordance with the quality assurance system at UiB and the department

Programme Committee

The Programme Committee is responsible for the content, structure and quality of the study programme and courses.

Course Coordinator

Course coordinator and administrative contact person can be found on Mitt UiB, or contact mailto:studieveileder@ii.uib.no">Student adviser

Course Administrator

The Faculty of Mathematics and Natural Sciences represented by the Department of Informatics is the course administrator for the course and study programme.

Contact Information

Student adviser:

mailto:studieveileder@ii.uib.no">Student adviser

T: 55 58 42 00

Exam information

  • For written exams, please note that the start time may change from 09:00 to 15:00 or vice versa until 14 days prior to the exam.

  • Type of assessment: Written exam

    21.02.2023, 09:00
    3 hours
    Withdrawal deadline
    Examination result announcement
    Examination system
    Digital exam