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.
  • Have basic knowledge of SQL
  • 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.

Admission to the course

IN-STK5000 and IN-STK9000 are viewed together in relation to admission and available spots. 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
  5. Other

Knowledge in probability (STK1000 – Introduction to Applied Statistics or STK1100 – Probability and Statistical Modelling) or Discrete Mathematics. Elementary calculus (differentiation, integration). Elementary programming skills (Python)

Some basic mathematical knowledge in probability (e.g. STK1100), linear algebra (e.g. MAT1120 – Linear Algebra) and algoritms (e.g. IN2010 – Algorithms and Data Structures)

Overlapping courses


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

More about mandatory assignments and other handouts.


Submission of mandatory assignments is required. All mandatory assignments prior to an exam must be approved before you can take the exam.

Exam format: Two group reports, based on two mini-projects, and a final oral/written exam.

Each report constitutes 40% of the final grade and the final exam constitutes 20% of the final grade. All parts must have a pass grade, and all parts must be passed in the same semester.

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.

Resit an examination

Students who can document a valid reason for absence from the regular examination are offered a postponed examination. 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) June 26, 2022 1:19:34 AM

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