IN-STK9000 – Adaptive methods for data-based decision making
Changes in the course due to coronavirus
Autumn 2020 and Spring 2021 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.
Please note that there may be changes in the form of examination for some courses taught Spring 2021. We aim to bring both the course description and the semester page of all courses up to date with correct information by 1 February 2021.
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.
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.
- Be able to critically read scientific papers in the area
- Be able to handle and mitigate issues related to privacy and fairness
- Know about the current research frontier in this area
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:
- PhD candidates who have the topic approved in their study plan
- Master´s students at the master´s program Computer Science who have passed the course approved in their curriculum
- Master´s students at the Faculty of Mathematics and Natural Sciences who have approved the subject in their curriculum
- Master's students at the Faculty of Mathematics and Natural Sciences
Recommended previous knowledge
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)
- 10 credits overlap with IN-STK5000 – Adaptive methods for data-based decision making.
4 hours of lectures/exercises/lab each week for the whole semester
Mandatory assignments, 2 group reports based on 2 mini-project (with report and/or presentation), and a final exam (oral or written depending on number of students). Mini-projects for the PhD course are expected to be of a higher complexity than for the equivalent master course.
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.
Grades are awarded on a pass/fail scale. 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.