Statistical Inference for Technological Applications

5 credits

Course, Master's level, 1TS325

Expand the information below to show details on how to apply and entry requirements.

Location
Uppsala
Pace of study
33%
Teaching form
On-campus
Instructional time
Daytime
Study period
18 January 2027–21 March 2027
Language of instruction
English
Entry requirements

Option 1: 120 credits whereof 60 credits in science/engineering, including 20 credits in mathematics, which should include single variable calculus, linear algebra and probability theory/statistics. Participation in Computer Programming I or Programming.

Option 2: 120 credits whereof 60 credits in science/engineering. Participation in 25 credits in mathematics/statistics/scientific computing, of which 15 credits should be completed. The 25 credits should include statistics. Participation in Computer Programming I or Programming.

Proficiency in English equivalent to the Swedish upper secondary course English 6.

Application deadline
15 October 2026
Application code
UU-64623

Admitted or on the waiting list?

Registration period
4 January 2027–17 January 2027
Information on registration from the department

Location
Uppsala
Pace of study
33%
Teaching form
On-campus
Instructional time
Daytime
Study period
18 January 2027–21 March 2027
Language of instruction
English
Entry requirements

Option 1: 120 credits whereof 60 credits in science/engineering, including 20 credits in mathematics, which should include single variable calculus, linear algebra and probability theory/statistics. Participation in Computer Programming I or Programming.

Option 2: 120 credits whereof 60 credits in science/engineering. Participation in 25 credits in mathematics/statistics/scientific computing, of which 15 credits should be completed. The 25 credits should include statistics. Participation in Computer Programming I or Programming.

Proficiency in English equivalent to the Swedish upper secondary course English 6.

Admitted or on the waiting list?

Registration period
4 January 2027–17 January 2027
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.

No reading list found.