Artificial Intelligence for Industrial Analytics
Course, Master's level, 1TS321
Expand the information below to show details on how to apply and entry requirements.
Autumn 2026 Autumn 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
- 2 November 2026–17 January 2027
- Language of instruction
- English
- Entry requirements
-
120 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. Proficiency in English equivalent to the Swedish upper secondary course English 6.
- Application deadline
- 15 April 2026
- Application code
- UU-14607
Admitted or on the waiting list?
- Registration period
- 19 October 2026–1 November 2026
- Information on registration from the department
Autumn 2026 Autumn 2026, Uppsala, 33%, On-campus, English For exchange students
- Location
- Uppsala
- Pace of study
- 33%
- Teaching form
- On-campus
- Instructional time
- Daytime
- Study period
- 2 November 2026–17 January 2027
- Language of instruction
- English
- Entry requirements
-
120 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. Proficiency in English equivalent to the Swedish upper secondary course English 6.
Admitted or on the waiting list?
- Registration period
- 19 October 2026–1 November 2026
- Information on registration from the department
About the course
This course will provide you with a broad and deep understanding of modern AI, with a focus on Intelligent Agents and Artificial Neural Networks (ANNs) for Industrial Analytics. It starts with widely applicable numerical methods used to rapidly get close to the best solution for a given optimization task. This foundation is then used to create different neural network-based Intelligent Agents suitable for Industrial Analytics. The main examples considered are often encountered in industrial applications such as signal + image analysis and challenging optimisation problems.