Advanced Image Analysis
Course, Master's level, 1MD037
Autumn 2023 Autumn 2023, Uppsala, 50%, On-campus, English Only available as part of a programme
- Location
- Uppsala
- Pace of study
- 50%
- Teaching form
- On-campus
- Instructional time
- Daytime
- Study period
- 31 October 2023–14 January 2024
- Language of instruction
- English
- Entry requirements
-
120 credits including Statistical Machine Learning, a second course in computer programming, Introduction to Image Analysis or Computer-Assisted Image Analysis I. Participation in Deep Learning for Image Analysis. Proficiency in English equivalent to the Swedish upper secondary course English 6.
- Application deadline
- 17 April 2023
- Application code
- UU-11623
Admitted or on the waiting list?
- Registration period
- 17 October 2023–7 November 2023
- Information on registration
Autumn 2024 Autumn 2024, Uppsala, 50%, On-campus, English Only available as part of a programme
- Location
- Uppsala
- Pace of study
- 50%
- Teaching form
- On-campus
- Instructional time
- Daytime
- Study period
- 4 November 2024–19 January 2025
- Language of instruction
- English
- Entry requirements
-
120 credits including Statistical Machine Learning, a second course in computer programming, Introduction to Image Analysis or Computer-Assisted Image Analysis I. Participation in Deep Learning for Image Analysis. Proficiency in English equivalent to the Swedish upper secondary course English 6.
- Application deadline
- 15 April 2024
- Application code
- UU-11623
Admitted or on the waiting list?
- Registration period
- 21 October 2024–11 November 2024
- Information on registration
About the course
The course is based on seminars, guest lectures, reading and discussion of scientific papers. The course contents are thematically adapted to the different specialisations within the Master's Programme in Image Analysis and Machine Learning and to some extent to the individual students' preferences. Topics that may be included in the course include texture analysis, image registration, and graph-based methods.
The course also contains reading assignments, oral and written presentations, critical analysis of scientific papers and methods, and discussions on ethical principles. Practical implementation and evaluation of methods for image analysis and machine learning may also be included.
Reading list
No reading list found.