IN-STK5000 – Adaptive Methods for Data-Based Decision Making

Schedule, syllabus and examination date

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Course content

Classic approaches in data analysis are based on a static (or predefined) procedure for both collecting and processing data. Modern approaches deal with the adaptive procedures which in practice almost always are used.

In this course you will learn how to design systems that adaptively collect and process data in order to make decisions autonomously or in collaboration with humans.

The course applies core principles from machine learning, artificial intelligence, databases and parallel computing to real-world problems in safety, reproducibility, transparency, privacy and fairness.

Learning outcome

After taking the course, you will:

  • See adaptive data analysis holistically, as a general decision problem.
  • Know how to adaptively plan data collection.
  • Understand when privacy is an issue and how to deal with privacy concerns.
  • Provide transparency by quantifying the strength of conclusions and ensuring reproducibility.
  • Be able to provide safety and reliability guarantees.
  • Have insight into issues of discrimination and fairness that can arise.
  • Be able to use large-scale data processing tools such as Tensor-Flow
  • Be able to deal with outliers, data contamination, etc.


Students admitted at UiO must apply for courses in Studentweb. Students enrolled in other Master's Degree Programmes can, on application, be admitted to the course if this is cleared by their own study programme.

Nordic citizens and applicants residing in the Nordic countries may apply to take this course as a single course student.

If you are not already enrolled as a student at UiO, please see our information about admission requirements and procedures for international applicants.

If the number of enrolled students is higher than the number of available spots, they will be ranked as follows:

1) PhD candidates who have the topic approved in their study plan

2) Master's students at the master's program Computer Science who have passed the course approved in their curriculum

3) Master's students at the Faculty of Mathematics and Natural Sciences who have approved the subject in their curriculum

4) Master's students at the Faculty of Mathematics and Natural Sciences



Recommended previous knowledge

Knowledge in probability (STK1100 – Probability and Statistical Modelling) or Discrete Mathematics. Elementary calculus (Differentiation, integration). Elementary programming skills (Python)

Some basic mathematical knowledge in probability (STK1100 – Probability and Statistical Modelling), linear algebra (MAT1120 – Linear algebra)

Overlapping courses

10 credits overlap with IN-STK9000 – Adaptive Methods for Data-Based Decision Making


4 hours of lectures/exercises/lab each week for the whole semester


Assignments, 3-4 group mini-project (with report and/or presentation), and oral or written exam.

Examination support material

All written material allowed

Language of examination

The examination text is given in English, and you submit your response in 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.

Explanations and appeals

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.

Withdrawal from an examination

It is possible to take the exam up to 3 times. If you withdraw from the exam after the deadline or during the exam, this will be counted as an examination attempt.

It will also be counted as one of your three attempts to sit the exam for this course, if you sit the exam for one of the following courses: IN-STK9000 – Adaptive Methods for Data-Based Decision Making

Special examination arrangements

Application form, deadline and requirements for special examination arrangements.

Facts about this course






Every autumn


Every autumn

Teaching language