FYS-STK3155 – Applied Data Analysis and Machine Learning

Schedule, syllabus and examination date

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Course content

The course introduces a variety of central algorithms and methods essential for studies of statistical 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 how to handle large scientific projects. 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 statistical data analysis and machine learning, with an emphasis on supervised learning.
  • have knowledge of central aspects of Monte Carlo methods, Markov chains, Gibbs samplers, data optimization, and their application.
  • understand linear and logistic regression methods.
  • understand central optimization algorithms like stochastic gradient descent methods.
  • have knowledge about neural networks and deep learning methods for both supervised and unsupervised learning.
  • have experience working on numerical projects.
  • have knowledge of other machine learning algorithms, such as decision trees, support vector machines, ensemble methods like random forests, bagging, and boosting.

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


The teaching is given throughout the semester: 

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


  • Home exams in the form of three project assignments that each counts 1/3 towards the final grade. 

When writing your exercises make sure to familiarize yourself with the rules for use of sources and citations. Breach of these rules may lead to suspicion of attempted cheating.

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

Students who can document a valid reason for absence from the regular examination are offered a postponed examination at the beginning of the next semester.

Re-scheduled examinations are not offered to students who withdraw during, or did not pass the original examination.

Special examination arrangements, use of sources, explanations and appeals

See more about examinations at UiO

Last updated from FS (Common Student System) May 27, 2022 4:11:38 AM

Facts about this course

Teaching language
Norwegian (English on request)