FYS-STK3155 – Applied Data Analysis and Machine Learning

Schedule, syllabus and examination date

Choose semester

Changes in the course due to coronavirus

Autumn 2020 the exams of most courses at the MN Faculty will be conducted as digital home exams or oral exams, using the normal grading scale. The semester page for your course will be updated with any changes in the form of examination.

See general guidelines for examination at the MN Faculty autumn 2020.

Course content

The course introduces a variety of central algorithms and methods essential for studies of data analysis and machine learning. The course is project based and through the various projects, the students will be exposed to fundamental research problems in these fields, with the aim to reproduce state of the art scientific results. The students will learn to develop and structure large codes for studying these systems, get acquainted with computing facilities and learn to handle large scientific projects. A good scientific and ethical conduct is emphasized throughout the course.

Learning outcome

After completing the course you will:

  • Have a basic understanding of Bayesian statistics and learning and common distributions
  • Have an understanding of central algorithms used in data analysis and machine learning
  • Have knowledge of central aspects of Monte Carlo methods, Markov chains, Gibbs samplers, data optimization, and their possible applications, from numerical integration to simulation of stock markets
  • Understand linear methods for regression and classification
  • Have knowledge about neural network, genetic algorithms and Boltzmann machines
  • Have experience from working on numerical projects.

Admission to the course

Students who are admitted to study programmes at UiO must each semester register which courses and exams they wish to sign up for in Studentweb.

Special admission requirements

In addition to fulfilling the Higher Education Entrance Qualification, applicants have to meet the following special admission requirements:

  • Mathematics R1 (or Mathematics S1 and S2) + R2

And in addition one of these:

  • Physics (1+2)
  • Chemistry (1+2)
  • Biology (1+2)
  • Information technology (1+2)
  • Geosciences (1+2)
  • Technology and theories of research (1+2)

The special admission requirements may also be covered by equivalent studies from Norwegian upper secondary school or by other equivalent studies (in Norwegian).

Basic knowledge in programming and numerics:

One, or more, of the following courses:

Overlapping courses


  • Four lectures per week, for about 15 weeks.
  • Four hours of laboratory sessions for work on computational projects per week, for about 15 weeks.
  • Weekly assignments.


  • Three mandatory project assignments that each counts 1/3 towards the final grade. 

It will also be counted as one of the three attempts to sit the exam for this course, if you sit the exam for one of the following courses: ​FYS-STK4155 – Applied Data Analysis and Machine Learning

Language of examination

Subjects taught in English will only offer the exam paper in English. You may write your examination paper in Norwegian, Swedish, Danish or English.

Grading scale

Grades are awarded on a scale from A to F, where A is the best grade and F is a fail. Read more about the grading system.

Resit an examination

This course offers both postponed and resit of examination. Read more:

Special examination arrangements, use of sources, explanations and appeals

See more about examinations at UiO

Last updated from FS (Common Student System) Nov. 24, 2020 6:11:18 PM

Facts about this course

Teaching language
Norwegian (English on request)