Statistical Inference for Technological Applications
Course, Master's level, 1TS325
Expand the information below to show details on how to apply and entry requirements.
Spring 2026
Spring 2026,
Uppsala, 33%, On-campus, English
Only available as part of a programme
- Location
- Uppsala
- Pace of study
- 33%
- Teaching form
- On-campus
- Instructional time
- Daytime
- Study period
- 19 January 2026–22 March 2026
- Language of instruction
- English
- Entry requirements
-
120 credits, of which 60 credits in science/engineering. Participation in courses of 25 credits in mathematics/statistics/scientific computing, of which 15 credits must be completed. Among these 25 credits, 5 credits in statistics should be included. Participation in a course in computer programming of 5 credits. Proficiency in English equivalent to the Swedish upper secondary course English 6.
- Application deadline
- 15 October 2025
- Application code
- UU-64623
Admitted or on the waiting list?
- Registration period
- 19 December 2025–19 January 2026
- Information on registration from the department
Spring 2026
Spring 2026,
Uppsala, 33%, On-campus, English
For exchange students
- Location
- Uppsala
- Pace of study
- 33%
- Teaching form
- On-campus
- Instructional time
- Daytime
- Study period
- 19 January 2026–22 March 2026
- Language of instruction
- English
- Entry requirements
-
120 credits, of which 60 credits in science/engineering. Participation in courses of 25 credits in mathematics/statistics/scientific computing, of which 15 credits must be completed. Among these 25 credits, 5 credits in statistics should be included. Participation in a course in computer programming of 5 credits. Proficiency in English equivalent to the Swedish upper secondary course English 6.
Admitted or on the waiting list?
- Registration period
- 19 December 2025–19 January 2026
- Information on registration from the department
About the course
In this course, you will learn about drawing industry-relevant conclusions from collected data (statistical inference) using two computer-intensive methods that often are easier and more powerful than the ones from Classical Statistics you have seen before. The first is almost theory-free and based on creating resampled datasets from the original one.
The second method is based on the Bayes theorem while interpreting distributions as measures of uncertainty. You will also study experimental design as well as how and why drawing conclusions using Machine Learning models differs from drawing conclusions using statistical data models.
Reading list
No reading list found.
Contact
- Study administration
- student-samint@angstrom.uu.se